Upload 18 files
Browse files- .gitattributes +1 -0
- README.md +182 -0
- bc.py +75 -0
- eval.csv +729 -0
- evaluate.py +188 -0
- plots.py +141 -0
- plots/clean_comparison.png +0 -0
- plots/four_agent.png +0 -0
- plots/training_curve.png +3 -0
- plots/trap_trajectories.png +0 -0
- policies.py +114 -0
- ppo_policy.pt +3 -0
- results.json +825 -0
- sim.py +441 -0
- train.py +206 -0
- training_curve.csv +301 -0
- training_curve_ft1.csv +301 -0
- training_curve_ft2.csv +301 -0
- training_curve_ft3.csv +301 -0
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README.md
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| 1 |
+
# Multi-agent RL navigation — trustworthy autonomous systems (simulation only)
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| 2 |
+
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| 3 |
+
**Repo name (not yet created): `multi-agent-rl-navigation`**
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| 4 |
+
**Status: local build only. No Hugging Face repo or Space has been created.**
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| 5 |
+
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+
A shared decentralized PPO policy for 2D multi-agent navigation, compared
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+
honestly against a textbook classical controller on the *same* seeded
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+
layouts. Built as a portfolio project for an application to Prof. Jiachen
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| 9 |
+
Li's Trustworthy Autonomous Systems Lab (Georgia Tech): robotics,
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+
trustworthy AI/ML, reinforcement learning, control, optimization and
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+
multi-agent systems.
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+
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+
## What this is — and is not
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+
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+
- **Is:** a self-contained 2D physics simulator (written for this project,
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+
no external environment, no hardware, no proprietary data) + a learned
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+
shared policy + a classical baseline + a fixed, seeded evaluation.
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+
- **Is not:** a robot result. No hardware was used. The RL policy has **no
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+
formal safety guarantee** — every safety number below is empirical,
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| 20 |
+
measured on the simulator's fixed test layouts. The wall-trap layout is
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+
deliberately constructed to defeat a pure potential-field controller.
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+
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+
## Simulator (`sim.py`)
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+
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+
- Arena 12x12 with walls. Agents: double-integrator kinematics,
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`dt=0.1`, max speed 1.2, max acceleration 3.0, agent radius 0.25,
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+
episode cap 200 steps.
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+
- Each layout: 3 circle obstacles (radius 0.5–0.8) **and** 1 wall/barrier
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+
segment. Two layout families, generated with fixed seeds:
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+
- *general*: a short (3 m) barrier segment at a random mid-arena angle.
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+
- *trap*: a vertical segment at x=6, y in [2,10], with gaps only at the
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ends; starts on the left, goals near-directly opposite on the right,
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+
so a direct potential field is attracted right and repelled left and
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+
stalls in the middle.
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+
- Decentralized 20-dim observation per agent: own velocity, relative goal
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+
vector + distance, nearest circle obstacle (direction, surface distance,
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+
radius), nearest wall segment (direction, distance, both endpoint
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+
vectors), nearest other agent (direction, distance), nearest arena-wall
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+
distance. (Endpoint vectors were added after a failed training run:
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| 40 |
+
with only the nearest segment point, the policy could not observe where
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the wall ends — see Limitations and `BUILD_REPORT.md`.)
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+
- Collision = distance to an obstacle/wall segment/arena wall below the
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+
agent radius, or agent–agent distance below 0.5. A colliding arrival is
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+
never counted as a success. Safety margin for violation counting: 0.15.
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+
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+
## Policies
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| 47 |
+
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+
- **Classical** (`policies.py`, exact gains documented in code): PD
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+
controller toward the goal (KD=2.0) + radial repulsive potential field
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+
(ETA=6.0, influence distance D0=2.0) from the nearest obstacle, segment
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+
and neighbour. No learning, no tangential escape trick — the honest
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textbook baseline. It stalls (timeout, 0 collisions) on every trap layout.
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+
- **RL**: shared decentralized PPO, MLP 20→128→128 (the observation grew
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+
from 15 to 20 dims when segment-endpoint features were added),
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+
Gaussian actions, tanh-bounded to the acceleration limit. One set of
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| 56 |
+
weights drives every agent from its own observation.
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+
Training recipe (all in `train.py`, PPO from scratch with torch only):
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| 58 |
+
the policy was warm-started by behaviour cloning the classical controller
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+
on general layouts only (400 episodes, 4 agents, final action MSE 0.079),
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| 60 |
+
then PPO fine-tuned in four phases — 1-agent mixed (trap ramp), 1-agent
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| 61 |
+
trap-only with an exploration reset, 1-agent mixed consolidation, and
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| 62 |
+
4-agent mixed — 300 iterations × 128-step rollouts × 16 envs per phase
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(614,400 environment steps per phase; 2,457,600 in the kept chain plus
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the BC dataset). Earlier from-scratch PPO runs failed outright (success
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5–15%) and are documented, with their fixes, in `BUILD_REPORT.md` —
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+
they are not hidden. The final phase's curve is `training_curve.csv`
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| 67 |
+
(earlier phases have their own `training_curve_*.csv` files).
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+
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## Evaluation protocol (exact)
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+
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+
Fixed layouts, identical for every policy and condition:
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+
- Single-agent: seeds 1000–1019 (10 general + 10 trap, alternating).
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| 73 |
+
- 4-agent: seeds 2000–2011 (6 general + 6 trap, alternating).
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| 74 |
+
- RL evaluated with the deterministic (mean) action; classical is
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| 75 |
+
deterministic. Noise conditions reuse the same noise RNG seed per
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| 76 |
+
layout/condition for both policies.
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+
- Conditions: clean; observation noise σ=0.05; observation noise σ=0.10;
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| 78 |
+
dynamics disturbance (added acceleration noise σ=1.0); obstacle shift
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+0.5 (circles and segments shifted +0.5 in x).
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+
- Metrics are per agent-episode (20 single-agent and 48 4-agent episodes
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| 81 |
+
per policy per condition; 728 rows in `eval.csv`). Steps-to-goal and
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+
clearance are reported on successes only; safety-violation rate is the
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+
fraction of active steps with clearance < 0.15.
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+
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+
## Results (copied from `results.json` — no other numbers are claimed)
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+
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+
### Clean
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+
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+
| Policy | Agents | Success | Collision | Obstacle coll. | Agent–agent coll. | Timeout | Steps (succ.) | Path len. | Min clearance (succ.) | Safety-viol. rate | General | Trap |
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+
|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| Classical | 1 | 0.450 | 0.000 | 0.000 | 0.000 | 0.550 | 73.22 | 5.35 | 0.588 | 0.000 | 0.90 | 0.00 |
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| RL | 1 | 0.700 | 0.200 | 0.200 | 0.000 | 0.100 | 86.00 | 9.59 | 0.419 | 0.033 | 0.60 | 0.80 |
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+
| Classical | 4 | 0.396 | 0.000 | 0.000 | 0.000 | 0.604 | 160.42 | 5.87 | 0.690 | 0.000 | 0.79 | 0.00 |
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+
| RL | 4 | 0.792 | 0.146 | 0.146 | 0.000 | 0.062 | 141.18 | 10.63 | 0.505 | 0.019 | 0.75 | 0.83 |
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+
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+
Reading, honestly: the RL policy wins on success everywhere, and is the
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only policy that solves trap layouts or coordinates 4 agents (zero
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agent–agent collisions clean). It loses on safety in the single-agent
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+
case: 20% collisions and lower clearance than the classical controller's
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0% collisions. The classical controller never collides clean — it fails
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by timing out at the wall instead. RL paths are longer (detours).
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+
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+
### Robustness (success / collision)
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+
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+
| Condition | RL 1-agent | Classical 1-agent | RL 4-agent | Classical 4-agent |
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+
|---|---|---|---|---|
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+
| Clean | 0.700 / 0.200 | 0.450 / 0.000 | 0.792 / 0.146 | 0.396 / 0.000 |
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| Obs. noise σ=0.05 | 0.750 / 0.150 | 0.450 / 0.100 | 0.646 / 0.292 | 0.333 / 0.125 |
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+
| Obs. noise σ=0.10 | 0.600 / 0.300 | 0.200 / 0.250 | 0.542 / 0.396 | 0.104 / 0.396 |
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+
| Dynamics disturbance σ=1.0 | 0.650 / 0.200 | 0.350 / 0.000 | 0.771 / 0.167 | 0.375 / 0.000 |
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+
| Obstacle shift +0.5 | 0.550 / 0.300 | 0.350 / 0.000 | 0.708 / 0.250 | 0.375 / 0.000 |
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+
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+
The RL policy degrades gracefully on success under every perturbation and
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+
stays well above classical, but its collision rate rises under noise
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| 115 |
+
(up to 0.396 at σ=0.10, 4-agent) — the trustworthy claim is *measured
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| 116 |
+
robustness of success*, not a safety proof.
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+
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+
### Ablation — neighbour observation, 4-agent clean
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+
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+
| Policy | Success | Collision | Agent–agent collision |
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+
|---|---|---|---|
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| RL with neighbour observation | 0.792 | 0.146 | 0.000 |
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| RL with neighbour observation zeroed | 0.146 | 0.708 | 0.333 |
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Removing the neighbour observation collapses coordination: success falls
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from 0.792 to 0.146 and agent–agent collisions jump from 0 to 0.333.
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| 127 |
+
The shared policy genuinely uses the neighbour channel; it is not
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+
four independent single-agent policies.
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+
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+
## Files in this repo
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+
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| 132 |
+
- `ppo_policy.pt` — final shared PPO weights (ActorCritic, obs dim 20).
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+
- `sim.py`, `policies.py`, `train.py`, `bc.py`, `evaluate.py`, `plots.py`
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| 134 |
+
- `results.json`, `eval.csv` (728 rows), `training_curve.csv` (final
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| 135 |
+
4-agent phase) + `training_curve_single.csv`,
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| 136 |
+
`training_curve_single2.csv`, `training_curve_ft1.csv`,
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| 137 |
+
`training_curve_ft2.csv`, `training_curve_ft3.csv` (earlier phases)
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| 138 |
+
- `plots/trap_trajectories.png`, `plots/four_agent.png`,
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| 139 |
+
`plots/clean_comparison.png`, `plots/training_curve.png`
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| 140 |
+
- `space/` — self-contained Gradio demo (copies of `sim.py`,
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| 141 |
+
`policies.py`, `ppo_policy.pt` + `app.py`, `requirements.txt`).
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| 142 |
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See `SMOKE_TEST.md`.
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+
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+
## Limitations
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| 145 |
+
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| 146 |
+
1. 2D simulation only. No hardware, no real robot, no vision (the
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+
observation is ground-truth state features + optional noise, not pixels).
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2. The wall trap is constructed. It demonstrates a known potential-field
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| 149 |
+
failure mode; it does not prove the policy generalises to arbitrary
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| 150 |
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unseen maps (the +0.5 obstacle-shift condition is the only unseen-layout
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| 151 |
+
test, and success there is 0.55–0.71, not 1.0).
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| 152 |
+
3. No formal safety guarantee. Collisions still occur (5–40% depending on
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| 153 |
+
condition). A safety shield / control-barrier filter would be the
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| 154 |
+
natural next step and is explicitly **not** claimed here.
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| 155 |
+
4. Small evaluation: 20 single-agent and 12 four-agent layouts. Differences
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| 156 |
+
of a few percent are within layout noise; the large effects (trap
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| 157 |
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0.00 → 0.80–0.83, 4-agent 0.396 → 0.792, ablation 0.792 → 0.146) are
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| 158 |
+
the ones to rely on.
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| 159 |
+
5. Training required a behaviour-cloning warm-start and a phased
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curriculum (including a trap-only phase that temporarily caused
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| 161 |
+
catastrophic forgetting of general layouts, fixed by a mixed
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| 162 |
+
consolidation phase). From-scratch PPO did not learn this task in
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| 163 |
+
~1.9M environment steps of attempts. See `BUILD_REPORT.md`.
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| 164 |
+
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| 165 |
+
## Reproduce
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| 167 |
+
```bash
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# shared venv (torch, numpy, matplotlib, gradio); proxy vars must be unset for gradio imports
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+
export VENV=~/workspace/rul-project/venv/bin/python
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+
env -u http_proxy -u https_proxy -u HTTP_PROXY -u HTTPS_PROXY -u all_proxy -u ALL_PROXY -u no_proxy -u NO_PROXY $VENV bc.py --episodes 400 --n-agents 4 --epochs 30 --out bc_policy.pt
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env -u ... $VENV train.py --iterations 300 --rollout 128 --n-agents 1 --init bc_policy.pt --out ppo_ft1.pt --curve training_curve_ft1.csv
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| 172 |
+
# (trap-only exploration-reset weights were made from ppo_ft1.pt by setting log_std to -0.5; see BUILD_REPORT.md)
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| 173 |
+
env -u ... $VENV train.py --iterations 300 --rollout 128 --n-agents 1 --init ppo_ft1_explore.pt --trap-prob 1.0 --trap-ramp-iters 1 --ent-coef 0.02 --out ppo_ft2.pt --curve training_curve_ft2.csv
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+
env -u ... $VENV train.py --iterations 300 --rollout 128 --n-agents 1 --init ppo_ft2.pt --trap-prob 0.5 --trap-ramp-iters 1 --out ppo_ft3.pt --curve training_curve_ft3.csv
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env -u ... $VENV train.py --iterations 300 --rollout 128 --n-agents 4 --init ppo_ft3.pt --trap-prob 0.5 --trap-ramp-iters 1 --out ppo_policy.pt --curve training_curve.csv
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env -u ... $VENV evaluate.py --weights ppo_policy.pt --out results.json --csv eval.csv
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env -u ... $VENV plots.py --weights ppo_policy.pt --results results.json --curve training_curve.csv --outdir plots
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+
```
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+
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+
All commands are run from `~/workspace/li-project/`. Fixed seeds throughout
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(training seed 0, BC seed 123, evaluation layout seeds 1000–1019 /
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2000–2011, noise seeds 90000+layout index).
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| 1 |
+
"""Behaviour-cloning warm-start from the classical controller.
|
| 2 |
+
|
| 3 |
+
Collects (observation, classical action) pairs by running the classical
|
| 4 |
+
PD + potential-field policy on GENERAL layouts only (trap=False, where
|
| 5 |
+
the teacher succeeds 10/10 in sanity checks). Trap layouts are excluded
|
| 6 |
+
deliberately: imitating the teacher there would teach the stall.
|
| 7 |
+
Trains only the actor (mean head) of ActorCritic with MSE on the
|
| 8 |
+
tanh-bounded action. The critic is left for PPO fine-tuning.
|
| 9 |
+
Outputs: bc_policy.pt, bc.log via stdout.
|
| 10 |
+
"""
|
| 11 |
+
import argparse
|
| 12 |
+
|
| 13 |
+
import numpy as np
|
| 14 |
+
import torch
|
| 15 |
+
import torch.nn as nn
|
| 16 |
+
|
| 17 |
+
from sim import AMAX, BatchedSim, generate_layout
|
| 18 |
+
from policies import ActorCritic, classical_action
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
def main():
|
| 22 |
+
ap = argparse.ArgumentParser()
|
| 23 |
+
ap.add_argument("--episodes", type=int, default=400)
|
| 24 |
+
ap.add_argument("--n-agents", type=int, default=4)
|
| 25 |
+
ap.add_argument("--epochs", type=int, default=30)
|
| 26 |
+
ap.add_argument("--seed", type=int, default=123)
|
| 27 |
+
ap.add_argument("--out", default="bc_policy.pt")
|
| 28 |
+
args = ap.parse_args()
|
| 29 |
+
torch.manual_seed(args.seed)
|
| 30 |
+
torch.set_num_threads(2)
|
| 31 |
+
rng = np.random.default_rng(args.seed)
|
| 32 |
+
sim = BatchedSim(16, args.n_agents, seed=args.seed)
|
| 33 |
+
obs_list, act_list = [], []
|
| 34 |
+
layouts = [generate_layout(rng, args.n_agents, trap=False) for _ in range(16)]
|
| 35 |
+
obs = sim.reset(layouts)
|
| 36 |
+
done_eps = 0
|
| 37 |
+
while done_eps < args.episodes:
|
| 38 |
+
act = classical_action(obs)
|
| 39 |
+
obs_list.append(obs.reshape(-1, obs.shape[-1]).copy())
|
| 40 |
+
act_list.append(act.reshape(-1, 2).copy())
|
| 41 |
+
obs, _, ep_done, _ = sim.step(act)
|
| 42 |
+
fin = np.where(ep_done)[0]
|
| 43 |
+
if len(fin):
|
| 44 |
+
done_eps += len(fin)
|
| 45 |
+
obs = sim.reset_envs(fin.tolist(),
|
| 46 |
+
[generate_layout(rng, args.n_agents, trap=False) for _ in fin])
|
| 47 |
+
O = torch.as_tensor(np.concatenate(obs_list, axis=0))
|
| 48 |
+
A = torch.as_tensor(np.concatenate(act_list, axis=0))
|
| 49 |
+
print(f"BC dataset: {O.shape[0]} samples, {done_eps} episodes", flush=True)
|
| 50 |
+
net = ActorCritic()
|
| 51 |
+
opt = torch.optim.Adam(list(net.actor.parameters()) + list(net.mean.parameters()), lr=1e-3)
|
| 52 |
+
n = O.shape[0]
|
| 53 |
+
for ep in range(args.epochs):
|
| 54 |
+
perm = torch.randperm(n)
|
| 55 |
+
tot = 0.0
|
| 56 |
+
for s in range(0, n, 1024):
|
| 57 |
+
b = perm[s:s + 1024]
|
| 58 |
+
mean, _ = net.forward(O[b])
|
| 59 |
+
pred = torch.tanh(mean) * AMAX
|
| 60 |
+
loss = nn.functional.mse_loss(pred, A[b])
|
| 61 |
+
opt.zero_grad(); loss.backward(); opt.step()
|
| 62 |
+
tot += float(loss) * len(b)
|
| 63 |
+
print(f"bc epoch {ep + 1}/{args.epochs} mse={tot / n:.4f}", flush=True)
|
| 64 |
+
# action agreement on a held-out-style check: mean |pred - teacher|
|
| 65 |
+
with torch.no_grad():
|
| 66 |
+
mean, _ = net.forward(O[:4096])
|
| 67 |
+
pred = torch.tanh(mean) * AMAX
|
| 68 |
+
print(f"BC final: mean |action error| = {float((pred - A[:4096]).abs().mean()):.3f} "
|
| 69 |
+
f"(accel units)", flush=True)
|
| 70 |
+
torch.save(net.state_dict(), args.out)
|
| 71 |
+
print(f"saved {args.out}")
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
if __name__ == "__main__":
|
| 75 |
+
main()
|
eval.csv
ADDED
|
@@ -0,0 +1,729 @@
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|
| 1 |
+
condition,policy,n_agents,trap,success,collision,collision_obstacle,collision_agent,timeout,steps,steps_total,path_length,min_clearance,viol_steps,active_steps
|
| 2 |
+
clean,classical,1,False,True,False,False,False,False,94,94,10.020771961247913,0.546326908958215,0,94
|
| 3 |
+
clean,classical,1,True,False,False,False,False,True,,200,5.169525920802393,0.4219333897809383,0,200
|
| 4 |
+
clean,classical,1,False,True,False,False,False,False,99,99,9.324993402289266,0.43233919377800867,0,99
|
| 5 |
+
clean,classical,1,True,False,False,False,False,True,,200,4.105784493667258,0.41943613337439967,0,200
|
| 6 |
+
clean,classical,1,False,True,False,False,False,False,69,69,7.01333839759433,0.690041335428711,0,69
|
| 7 |
+
clean,classical,1,True,False,False,False,False,True,,200,2.8057803452869883,0.42101202070805144,0,200
|
| 8 |
+
clean,classical,1,False,True,False,False,False,False,50,50,4.380144797191369,0.5971487872750222,0,50
|
| 9 |
+
clean,classical,1,True,False,False,False,False,True,,200,3.007057990903835,0.42093323225245705,0,200
|
| 10 |
+
clean,classical,1,False,True,False,False,False,False,62,62,6.20966437396852,0.6123711478961167,0,62
|
| 11 |
+
clean,classical,1,True,False,False,False,False,True,,200,4.145466370095655,0.42173522180126355,0,200
|
| 12 |
+
clean,classical,1,False,True,False,False,False,False,47,47,4.821477610004144,0.742755171556448,0,47
|
| 13 |
+
clean,classical,1,True,False,False,False,False,True,,200,3.6601198326222524,0.4216866235067034,0,200
|
| 14 |
+
clean,classical,1,False,True,False,False,False,False,57,57,5.904894340296492,0.755567417500739,0,57
|
| 15 |
+
clean,classical,1,True,False,False,False,False,True,,200,4.48854699396839,0.41947137666812306,0,200
|
| 16 |
+
clean,classical,1,False,False,False,False,False,True,,200,5.682721859275976,0.44812407142268396,0,200
|
| 17 |
+
clean,classical,1,True,False,False,False,False,True,,200,3.4071123315421046,0.42079009747400153,0,200
|
| 18 |
+
clean,classical,1,False,True,False,False,False,False,76,76,7.345775240112223,0.48243075214285014,0,76
|
| 19 |
+
clean,classical,1,True,False,False,False,False,True,,200,3.127851662980229,0.42231477956304175,0,200
|
| 20 |
+
clean,classical,1,False,True,False,False,False,False,105,105,8.369489507532391,0.4369587328545136,0,105
|
| 21 |
+
clean,classical,1,True,False,False,False,False,True,,200,3.9665929829612385,0.42194872688989893,0,200
|
| 22 |
+
clean,rl,1,False,True,False,False,False,False,89,89,10.522623036219125,0.2615037707997643,0,89
|
| 23 |
+
clean,rl,1,True,True,False,False,False,False,104,104,12.310932938485898,0.32334089518020814,0,104
|
| 24 |
+
clean,rl,1,False,False,True,True,False,False,,23,2.5416358212891548,-0.06782512142693234,3,23
|
| 25 |
+
clean,rl,1,True,True,False,False,False,False,88,88,10.419936044331504,0.2810461371893699,0,88
|
| 26 |
+
clean,rl,1,False,True,False,False,False,False,59,59,6.944336882528997,0.9882004144358216,0,59
|
| 27 |
+
clean,rl,1,True,True,False,False,False,False,79,79,9.340178148885167,0.38275370811130094,0,79
|
| 28 |
+
clean,rl,1,False,True,False,False,False,False,72,72,7.384959051411224,0.025130704345566968,8,72
|
| 29 |
+
clean,rl,1,True,False,False,False,False,True,,200,14.882863777281507,0.2416333617458264,0,200
|
| 30 |
+
clean,rl,1,False,True,False,False,False,False,52,52,6.072709501651292,0.0693744558381546,7,52
|
| 31 |
+
clean,rl,1,True,True,False,False,False,False,93,93,11.000089604400262,0.4613841637356543,0,93
|
| 32 |
+
clean,rl,1,False,True,False,False,False,False,43,43,5.0242956363901214,0.742755171556448,0,43
|
| 33 |
+
clean,rl,1,True,True,False,False,False,False,100,100,11.756482226002301,0.17720193736913536,0,100
|
| 34 |
+
clean,rl,1,False,True,False,False,False,False,52,52,6.064545778106935,1.1787503600108389,0,52
|
| 35 |
+
clean,rl,1,True,False,True,True,False,False,,82,9.672160886235021,-0.03929776237632887,4,82
|
| 36 |
+
clean,rl,1,False,False,True,True,False,False,,26,2.9598608609070167,-0.03494377441447061,2,26
|
| 37 |
+
clean,rl,1,True,True,False,False,False,False,107,107,12.317005144079708,0.4961837624908423,0,107
|
| 38 |
+
clean,rl,1,False,False,False,False,False,True,,200,20.55580084464249,0.0005946952712083675,25,200
|
| 39 |
+
clean,rl,1,True,True,False,False,False,False,176,176,17.87536869199227,0.1225990905026908,6,176
|
| 40 |
+
clean,rl,1,False,False,True,True,False,False,,30,3.4291128001867044,-0.08604517540513199,3,30
|
| 41 |
+
clean,rl,1,True,True,False,False,False,False,90,90,10.680880486767856,0.35026066445746284,0,90
|
| 42 |
+
clean,classical,4,False,False,False,False,False,True,,200,2.7963605828593567,0.4767303353740905,0,200
|
| 43 |
+
clean,classical,4,False,True,False,False,False,False,200,200,6.986250431908333,0.4546631452983244,0,104
|
| 44 |
+
clean,classical,4,False,True,False,False,False,False,200,200,8.551406214610694,0.4265091864039837,0,133
|
| 45 |
+
clean,classical,4,False,True,False,False,False,False,200,200,4.2393750701340975,1.4599493841971147,0,42
|
| 46 |
+
clean,classical,4,True,False,False,False,False,True,,200,4.994171060881629,0.38840955030012747,0,200
|
| 47 |
+
clean,classical,4,True,False,False,False,False,True,,200,6.047674914083485,0.38840955030012747,0,200
|
| 48 |
+
clean,classical,4,True,False,False,False,False,True,,200,4.572081268301781,0.3604595306300542,0,200
|
| 49 |
+
clean,classical,4,True,False,False,False,False,True,,200,4.135459616899709,0.47347131177808954,0,200
|
| 50 |
+
clean,classical,4,False,True,False,False,False,False,169,169,6.263870983076489,0.5184039392588594,0,66
|
| 51 |
+
clean,classical,4,False,True,False,False,False,False,169,169,8.136873826868236,0.4940861647902264,0,169
|
| 52 |
+
clean,classical,4,False,True,False,False,False,False,169,169,4.67104231378668,0.8093058465542056,0,69
|
| 53 |
+
clean,classical,4,False,True,False,False,False,False,169,169,8.602079674883823,0.6976485291714835,0,81
|
| 54 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.453369164053677,0.3243250210810187,0,200
|
| 55 |
+
clean,classical,4,True,False,False,False,False,True,,200,6.586327388401598,0.3709159077265506,0,200
|
| 56 |
+
clean,classical,4,True,False,False,False,False,True,,200,6.445516310954832,0.3709159077265506,0,200
|
| 57 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.367903231537469,0.5894052535310701,0,200
|
| 58 |
+
clean,classical,4,False,True,False,False,False,False,200,200,5.511887610241219,0.982091697325675,0,63
|
| 59 |
+
clean,classical,4,False,True,False,False,False,False,200,200,3.710463251421163,0.9281944272080676,0,38
|
| 60 |
+
clean,classical,4,False,False,False,False,False,True,,200,2.3658524159328342,0.4235720069092799,0,200
|
| 61 |
+
clean,classical,4,False,True,False,False,False,False,200,200,7.729076561327276,0.4261446738542868,0,92
|
| 62 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.344988019761774,0.5116601061869446,0,200
|
| 63 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.6659716506036,0.34666373118499116,0,200
|
| 64 |
+
clean,classical,4,True,False,False,False,False,True,,200,4.53189344304445,0.3681432758965437,0,200
|
| 65 |
+
clean,classical,4,True,False,False,False,False,True,,200,4.216432819635722,0.4287999147557633,0,200
|
| 66 |
+
clean,classical,4,False,True,False,False,False,False,90,90,4.511108822516079,0.6485088958800709,0,50
|
| 67 |
+
clean,classical,4,False,True,False,False,False,False,90,90,6.294639716594764,0.580073101180187,0,62
|
| 68 |
+
clean,classical,4,False,True,False,False,False,False,90,90,5.651408595076293,0.536157434409869,0,88
|
| 69 |
+
clean,classical,4,False,True,False,False,False,False,90,90,9.751303163388487,0.8614251823519197,0,90
|
| 70 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.701788669613694,0.5230459543079578,0,200
|
| 71 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.690940526107635,0.3590294496376263,0,200
|
| 72 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.184924457782425,0.36895365235752464,0,200
|
| 73 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.071954412558878,0.5137875854254643,0,200
|
| 74 |
+
clean,classical,4,False,True,False,False,False,False,153,153,8.16999972686336,0.5580041351064334,0,132
|
| 75 |
+
clean,classical,4,False,True,False,False,False,False,153,153,10.849673535983749,0.5428131221752216,0,147
|
| 76 |
+
clean,classical,4,False,True,False,False,False,False,153,153,6.879570498427198,0.5681789360589111,0,71
|
| 77 |
+
clean,classical,4,False,True,False,False,False,False,153,153,10.911260903485969,0.5280775769099225,0,153
|
| 78 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.25070037658534,0.42285822155643515,0,200
|
| 79 |
+
clean,classical,4,True,False,False,False,False,True,,200,4.880329640441414,0.5023476703184437,0,200
|
| 80 |
+
clean,classical,4,True,False,False,False,False,True,,200,4.980805500715881,0.3652930139976114,0,200
|
| 81 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.182775361576883,0.5146298318565456,0,200
|
| 82 |
+
clean,classical,4,False,False,False,False,False,True,,200,4.313431211573427,0.42093921178385896,0,200
|
| 83 |
+
clean,classical,4,False,True,False,False,False,False,200,200,4.806458989974312,1.0881780763824616,0,50
|
| 84 |
+
clean,classical,4,False,False,False,False,False,True,,200,6.321011623683018,0.38149835149671807,0,200
|
| 85 |
+
clean,classical,4,False,False,False,False,False,True,,200,5.368590692633524,0.6260134477033296,0,200
|
| 86 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.598506976003924,0.38455573410975175,0,200
|
| 87 |
+
clean,classical,4,True,False,False,False,False,True,,200,6.849631527876919,0.38455573410975175,0,200
|
| 88 |
+
clean,classical,4,True,False,False,False,False,True,,200,5.977346617877274,0.340687510518662,0,200
|
| 89 |
+
clean,classical,4,True,False,False,False,False,True,,200,4.70232954621755,0.340687510518662,0,200
|
| 90 |
+
clean,rl,4,False,True,False,False,False,False,98,98,7.762102398196188,0.5172825987399546,0,66
|
| 91 |
+
clean,rl,4,False,True,False,False,False,False,98,98,7.154863160633348,0.7665011183525254,0,61
|
| 92 |
+
clean,rl,4,False,True,False,False,False,False,98,98,9.685738230358519,0.2634562629349966,0,98
|
| 93 |
+
clean,rl,4,False,True,False,False,False,False,98,98,4.554560731429564,1.4599493841971147,0,39
|
| 94 |
+
clean,rl,4,True,True,False,False,False,False,200,200,10.064141335082784,0.38840955030012747,0,85
|
| 95 |
+
clean,rl,4,True,True,False,False,False,False,200,200,12.480815493099259,0.38840955030012747,0,112
|
| 96 |
+
clean,rl,4,True,True,False,False,False,False,200,200,15.98415243012207,0.5532537013490509,0,158
|
| 97 |
+
clean,rl,4,True,False,False,False,False,True,,200,16.34935865519159,0.004579682898284254,33,200
|
| 98 |
+
clean,rl,4,False,True,False,False,False,False,52,52,6.0527179830830775,0.7998003052751208,0,52
|
| 99 |
+
clean,rl,4,False,False,True,True,False,False,,52,1.1390973626581045,-0.06394506062837702,3,11
|
| 100 |
+
clean,rl,4,False,True,False,False,False,False,52,52,4.3797151564584444,0.7236610468645288,0,38
|
| 101 |
+
clean,rl,4,False,False,True,True,False,False,,52,4.3234815127589945,-0.024633183257895958,3,37
|
| 102 |
+
clean,rl,4,True,True,False,False,False,False,196,196,14.260156835315222,0.6345352012987289,0,123
|
| 103 |
+
clean,rl,4,True,True,False,False,False,False,196,196,14.145499780397337,0.3709159077265506,0,120
|
| 104 |
+
clean,rl,4,True,True,False,False,False,False,196,196,16.05392768781351,0.22736175267914005,0,140
|
| 105 |
+
clean,rl,4,True,True,False,False,False,False,196,196,18.887581125229865,0.22736175267914005,0,196
|
| 106 |
+
clean,rl,4,False,True,False,False,False,False,65,65,5.213838343837576,1.3791961378174484,0,45
|
| 107 |
+
clean,rl,4,False,True,False,False,False,False,65,65,3.8996263147288994,1.2247975678733494,0,34
|
| 108 |
+
clean,rl,4,False,False,True,True,False,False,,65,2.442741512168106,-0.07052409492857184,2,22
|
| 109 |
+
clean,rl,4,False,True,False,False,False,False,65,65,7.640945310173387,0.39778525178127666,0,65
|
| 110 |
+
clean,rl,4,True,True,False,False,False,False,145,145,16.75374717703473,0.2671146239936182,0,145
|
| 111 |
+
clean,rl,4,True,True,False,False,False,False,145,145,15.137076444302478,0.025054214966989008,9,138
|
| 112 |
+
clean,rl,4,True,False,True,True,False,False,,145,13.390824268857628,-0.019583477948855066,2,141
|
| 113 |
+
clean,rl,4,True,False,True,True,False,False,,145,4.647732364909702,-0.00018426559163664535,6,40
|
| 114 |
+
clean,rl,4,False,True,False,False,False,False,117,117,4.812975082939627,0.30853539419958576,0,42
|
| 115 |
+
clean,rl,4,False,True,False,False,False,False,117,117,10.729008075208768,0.0501162253405667,7,117
|
| 116 |
+
clean,rl,4,False,True,False,False,False,False,117,117,4.899929355878439,0.30853539419958576,0,46
|
| 117 |
+
clean,rl,4,False,True,False,False,False,False,117,117,9.961433121445442,0.8398719317564187,0,84
|
| 118 |
+
clean,rl,4,True,True,False,False,False,False,200,200,15.188315290451506,0.4355746617404527,0,128
|
| 119 |
+
clean,rl,4,True,True,False,False,False,False,200,200,14.913564524921393,0.36895365235752464,0,138
|
| 120 |
+
clean,rl,4,True,False,False,False,False,True,,200,18.976762645694425,0.23896627607641374,0,200
|
| 121 |
+
clean,rl,4,True,True,False,False,False,False,200,200,16.508768591100964,0.5202106082262423,0,160
|
| 122 |
+
clean,rl,4,False,True,False,False,False,False,96,96,7.215381507669809,0.34779735441627513,0,70
|
| 123 |
+
clean,rl,4,False,False,True,True,False,False,,96,5.248029335105978,-0.08314345022256686,3,45
|
| 124 |
+
clean,rl,4,False,True,False,False,False,False,96,96,6.429700453651664,0.10681281662190312,5,55
|
| 125 |
+
clean,rl,4,False,True,False,False,False,False,96,96,10.797694717560546,0.46794729728484863,0,96
|
| 126 |
+
clean,rl,4,True,True,False,False,False,False,152,152,11.279508923158556,0.7424154093729131,0,95
|
| 127 |
+
clean,rl,4,True,True,False,False,False,False,152,152,15.613340701462688,0.5631017201494779,0,132
|
| 128 |
+
clean,rl,4,True,True,False,False,False,False,152,152,15.888538689121301,0.5146298318565456,0,143
|
| 129 |
+
clean,rl,4,True,True,False,False,False,False,152,152,16.057775692745082,0.5146298318565456,0,152
|
| 130 |
+
clean,rl,4,False,False,True,True,False,False,,200,2.1495797892659607,-0.04719372304389258,3,19
|
| 131 |
+
clean,rl,4,False,True,False,False,False,False,200,200,4.919266269370023,1.0807103453693554,0,42
|
| 132 |
+
clean,rl,4,False,True,False,False,False,False,200,200,8.959911863875375,0.7607772293538262,0,76
|
| 133 |
+
clean,rl,4,False,False,False,False,False,True,,200,13.632583771175117,0.6547102902135493,0,200
|
| 134 |
+
clean,rl,4,True,True,False,False,False,False,159,159,12.919013258209448,0.09519043582934428,8,109
|
| 135 |
+
clean,rl,4,True,True,False,False,False,False,159,159,15.361425173121194,0.18257649425193223,0,129
|
| 136 |
+
clean,rl,4,True,True,False,False,False,False,159,159,18.48674729677409,0.013971262382182159,8,159
|
| 137 |
+
clean,rl,4,True,True,False,False,False,False,159,159,17.05794570430714,0.340687510518662,0,145
|
| 138 |
+
obs_noise_0.05,classical,1,False,True,False,False,False,False,109,109,10.796703602226486,0.6433411501130967,0,109
|
| 139 |
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disturbance_1.0,rl,4,False,True,False,False,False,False,94,94,6.4350617391292735,0.07380907029521522,7,55
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| 533 |
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disturbance_1.0,rl,4,False,True,False,False,False,False,94,94,10.77047351952304,0.6272093616986214,0,94
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disturbance_1.0,rl,4,True,True,False,False,False,False,157,157,11.476680643775536,0.6100230522743515,0,97
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disturbance_1.0,rl,4,True,True,False,False,False,False,157,157,15.60202586769994,0.46168252894420103,0,134
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| 536 |
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disturbance_1.0,rl,4,True,True,False,False,False,False,157,157,16.02201022332059,0.45470143530158,0,145
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| 537 |
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disturbance_1.0,rl,4,True,True,False,False,False,False,157,157,15.891920711451018,0.5146298318565456,0,157
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| 538 |
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disturbance_1.0,rl,4,False,False,True,True,False,False,,92,2.000398065321864,-0.08024416639411536,2,18
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| 539 |
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disturbance_1.0,rl,4,False,True,False,False,False,False,92,92,5.006136348032467,1.0651730340738257,0,43
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| 540 |
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disturbance_1.0,rl,4,False,True,False,False,False,False,92,92,8.79325165395686,0.6771081366035181,0,75
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| 541 |
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disturbance_1.0,rl,4,False,True,False,False,False,False,92,92,9.805319534086886,0.5407165364387174,0,92
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| 542 |
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disturbance_1.0,rl,4,True,True,False,False,False,False,149,149,12.971276659063975,0.19927171345101768,0,110
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| 543 |
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disturbance_1.0,rl,4,True,False,True,True,False,False,,149,12.313016609581782,-0.016488921881164686,3,104
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disturbance_1.0,rl,4,True,False,True,True,False,False,,149,13.30919738034036,-0.044308177274152705,4,119
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| 545 |
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disturbance_1.0,rl,4,True,True,False,False,False,False,149,149,17.148403130355202,0.14504735419804837,2,149
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| 546 |
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obstacle_shift_0.5,classical,1,False,True,False,False,False,False,89,89,9.733676871678645,0.7623338655444365,0,89
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| 547 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,5.642888373909634,0.42323257108208256,0,200
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| 548 |
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obstacle_shift_0.5,classical,1,False,False,False,False,False,True,,200,3.8623374671062995,0.45180384135825147,0,200
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| 549 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,4.6052192461790105,0.4198832592818098,0,200
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| 550 |
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obstacle_shift_0.5,classical,1,False,True,False,False,False,False,78,78,7.44393559564574,0.5861496635936626,0,78
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| 551 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,3.299725745858276,0.41979044365323137,0,200
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| 552 |
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obstacle_shift_0.5,classical,1,False,True,False,False,False,False,42,42,4.009939025268544,0.7448045768975305,0,42
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| 553 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,3.5036956886103536,0.4197171020144319,0,200
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| 554 |
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obstacle_shift_0.5,classical,1,False,True,False,False,False,False,79,79,6.8450982719154965,0.3134184244862759,0,79
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| 555 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,4.6883060585606655,0.42174390763142,0,200
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| 556 |
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obstacle_shift_0.5,classical,1,False,True,False,False,False,False,47,47,4.821649965513695,0.742755171556448,0,47
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| 557 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,4.1403252900396135,0.421982394554143,0,200
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| 558 |
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obstacle_shift_0.5,classical,1,False,True,False,False,False,False,55,55,5.789979019874943,1.0294693620509994,0,55
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| 559 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,4.982051199883149,0.41981171130638906,0,200
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| 560 |
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obstacle_shift_0.5,classical,1,False,False,False,False,False,True,,200,5.331658931178394,0.4445790196503845,0,200
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| 561 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,3.9030683520328835,0.4216323429397173,0,200
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| 562 |
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obstacle_shift_0.5,classical,1,False,False,False,False,False,True,,200,6.524220996532792,0.4227409627807682,0,200
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| 563 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,3.608740823602048,0.42085616721788277,0,200
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| 564 |
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obstacle_shift_0.5,classical,1,False,True,False,False,False,False,103,103,8.330147983123009,0.4373915255877697,0,103
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| 565 |
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obstacle_shift_0.5,classical,1,True,False,False,False,False,True,,200,4.478716770923448,0.42013123921325857,0,200
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| 566 |
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obstacle_shift_0.5,rl,1,False,False,True,True,False,False,,30,3.4351939787933503,-0.03262355188686117,4,30
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| 567 |
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obstacle_shift_0.5,rl,1,True,True,False,False,False,False,106,106,12.571310336742112,0.38928944785857855,0,106
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| 568 |
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obstacle_shift_0.5,rl,1,False,False,True,True,False,False,,23,2.4862835823905125,-0.027626770768517106,3,23
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| 569 |
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obstacle_shift_0.5,rl,1,True,True,False,False,False,False,90,90,10.656078247198948,0.28728665664459796,0,90
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| 570 |
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obstacle_shift_0.5,rl,1,False,True,False,False,False,False,60,60,7.062341098642879,0.662436212994353,0,60
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| 571 |
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obstacle_shift_0.5,rl,1,True,True,False,False,False,False,80,80,9.459861013038758,0.42915394371017723,0,80
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| 572 |
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obstacle_shift_0.5,rl,1,False,True,False,False,False,False,34,34,3.9159102664931527,0.3998229252910025,0,34
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| 573 |
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obstacle_shift_0.5,rl,1,True,False,False,False,False,True,,200,13.396532362243107,0.3495023673306754,0,200
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| 574 |
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obstacle_shift_0.5,rl,1,False,False,True,True,False,False,,20,2.232692887958099,-0.06996280696903989,3,20
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| 575 |
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obstacle_shift_0.5,rl,1,True,True,False,False,False,False,96,96,11.371929206188247,0.5685336942001702,0,96
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| 576 |
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obstacle_shift_0.5,rl,1,False,True,False,False,False,False,43,43,5.019945485827109,0.742755171556448,0,43
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| 577 |
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obstacle_shift_0.5,rl,1,True,True,False,False,False,False,102,102,11.932240488849423,0.2456882211420559,0,102
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| 578 |
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obstacle_shift_0.5,rl,1,False,True,False,False,False,False,52,52,6.0802914616950305,1.0892222219432834,0,52
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| 579 |
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obstacle_shift_0.5,rl,1,True,False,True,True,False,False,,87,10.256808154392186,-0.03891838586264229,3,87
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| 580 |
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obstacle_shift_0.5,rl,1,False,False,True,True,False,False,,21,2.3568641357695754,-0.05594713959506259,2,21
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| 581 |
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obstacle_shift_0.5,rl,1,True,True,False,False,False,False,110,110,12.701214015253477,0.5104412953421658,0,110
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| 582 |
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obstacle_shift_0.5,rl,1,False,False,False,False,False,True,,200,19.409323390717564,0.242762634429653,0,200
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| 583 |
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obstacle_shift_0.5,rl,1,True,False,False,False,False,True,,200,17.313757992370196,0.19918232343048725,0,200
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| 584 |
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obstacle_shift_0.5,rl,1,False,False,True,True,False,False,,25,2.8259301248661686,-0.003319278269155207,2,25
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| 585 |
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obstacle_shift_0.5,rl,1,True,True,False,False,False,False,89,89,10.560559844041812,0.3110963659425492,0,89
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| 586 |
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obstacle_shift_0.5,classical,4,False,False,False,False,False,True,,200,3.363900056135701,0.44905715661417533,0,200
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| 587 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,8.043037555333477,0.4780410113250403,0,141
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| 588 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,9.065012812889712,0.42684030006414864,0,170
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| 589 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,4.241490143713373,1.4599493841971147,0,42
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| 590 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.464267650366084,0.38840955030012747,0,200
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| 591 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,6.598948879708025,0.38840955030012747,0,200
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| 592 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.139482744424559,0.37240806151339356,0,200
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| 593 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,4.594062289822891,0.48197031624640996,0,200
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| 594 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,6.38612563062531,0.36727349991779146,0,65
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| 595 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,6.7608372427150245,0.5028442062709726,0,129
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| 596 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,5.78612282275016,0.6801542996415461,0,112
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| 597 |
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obstacle_shift_0.5,classical,4,False,False,False,False,False,True,,200,9.045877415134663,0.6782085037225274,0,200
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| 598 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.988459902387667,0.32783136587433503,0,200
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| 599 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,7.040494657425791,0.3709159077265506,0,200
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| 600 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,6.99737086749244,0.3709159077265506,0,200
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| 601 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.73213387386439,0.6132947095633501,0,200
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| 602 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,5.248456639615057,1.1653329203040634,0,52
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| 603 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,3.657015353389362,1.2314829040610298,0,37
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| 604 |
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obstacle_shift_0.5,classical,4,False,False,False,False,False,True,,200,1.9343964634539899,0.4309911180255912,0,200
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| 605 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,8.206265107279686,0.4215877044475612,0,130
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| 606 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.667542921426092,0.5160248470018667,0,200
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| 607 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,6.088593837317935,0.38562819508584134,0,200
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| 608 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,4.980075755073205,0.3720552667660417,0,200
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| 609 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,4.626432974672021,0.4308954680834862,0,200
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| 610 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,118,118,4.9711524301054455,0.4848858926522783,0,66
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| 611 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,118,118,6.784730976174554,0.48968224057180343,0,72
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| 612 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,118,118,6.476020075619379,0.47533199619891675,0,109
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| 613 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,118,118,10.124932327488755,0.7657470743106007,0,118
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| 614 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,6.164179025627288,0.5181291770237433,0,200
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| 615 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.953628083955092,0.3467599197479032,0,200
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| 616 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.69163439475385,0.36895365235752464,0,200
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| 617 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.612084129155074,0.5429182252225404,0,200
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| 618 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,98,98,7.069250861425965,0.5431456669782332,0,81
|
| 619 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,98,98,8.928773036575775,0.5661749127231355,0,86
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| 620 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,98,98,7.6092173856801955,0.45833533086192335,0,90
|
| 621 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,98,98,9.567535654220181,0.8306820716490695,0,98
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| 622 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.753917676870737,0.4214503168900503,0,200
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| 623 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.314741648371622,0.514005657418048,0,200
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| 624 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.477029720342921,0.3576453937080819,0,200
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| 625 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.584134672985038,0.5146298318565456,0,200
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| 626 |
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obstacle_shift_0.5,classical,4,False,False,False,False,False,True,,200,4.845164229769451,0.43472981411603706,0,200
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| 627 |
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obstacle_shift_0.5,classical,4,False,True,False,False,False,False,200,200,4.907652966418282,1.0762336844966922,0,52
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| 628 |
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obstacle_shift_0.5,classical,4,False,False,False,False,False,True,,200,6.602532793829559,0.38558785955057506,0,200
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| 629 |
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obstacle_shift_0.5,classical,4,False,False,False,False,False,True,,200,5.697307031724473,0.5630992061509129,0,200
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| 630 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,6.213980217706688,0.34339351255183637,0,200
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| 631 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,7.699164964274809,0.38455573410975175,0,200
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| 632 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,6.4020559313368395,0.340687510518662,0,200
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| 633 |
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obstacle_shift_0.5,classical,4,True,False,False,False,False,True,,200,5.5948498160572075,0.3258207185035351,0,200
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| 634 |
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obstacle_shift_0.5,rl,4,False,True,False,False,False,False,111,111,11.23271696290905,0.7051771010830349,0,111
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| 635 |
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obstacle_shift_0.5,rl,4,False,True,False,False,False,False,111,111,9.209506012846417,0.8395443005527472,0,85
|
| 636 |
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obstacle_shift_0.5,rl,4,False,True,False,False,False,False,111,111,10.493424233370638,0.07838612763973551,5,105
|
| 637 |
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obstacle_shift_0.5,rl,4,False,True,False,False,False,False,111,111,4.555269928613153,1.4599493841971147,0,39
|
| 638 |
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obstacle_shift_0.5,rl,4,True,True,False,False,False,False,197,197,10.195931058958742,0.38840955030012747,0,86
|
| 639 |
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obstacle_shift_0.5,rl,4,True,True,False,False,False,False,197,197,12.48721034275798,0.38840955030012747,0,109
|
| 640 |
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obstacle_shift_0.5,rl,4,True,True,False,False,False,False,197,197,15.571203854456954,0.45691070226517805,0,168
|
| 641 |
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obstacle_shift_0.5,rl,4,True,True,False,False,False,False,197,197,16.923176984985254,0.38308125783605074,0,197
|
| 642 |
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obstacle_shift_0.5,rl,4,False,True,False,False,False,False,83,83,7.470342259105109,0.9431197116212868,0,83
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| 643 |
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obstacle_shift_0.5,rl,4,False,False,True,True,False,False,,83,1.6419723195051998,-0.03551841756711316,4,15
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| 644 |
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obstacle_shift_0.5,rl,4,False,True,False,False,False,False,83,83,4.3846716173994125,1.0712050212737099,0,38
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| 645 |
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obstacle_shift_0.5,rl,4,False,True,False,False,False,False,83,83,8.763783577611633,0.35343798342025223,0,74
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| 646 |
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obstacle_shift_0.5,rl,4,True,True,False,False,False,False,148,148,14.932895466109095,0.5074895012555494,0,128
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| 647 |
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obstacle_shift_0.5,rl,4,True,True,False,False,False,False,148,148,14.587185682508595,0.26623615258458166,0,126
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| 648 |
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obstacle_shift_0.5,rl,4,True,True,False,False,False,False,148,148,16.70905236366369,0.20533524499595124,0,148
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| 649 |
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obstacle_shift_0.5,rl,4,True,True,False,False,False,False,148,148,15.366721093195778,0.20533524499595124,0,144
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| 650 |
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obstacle_shift_0.5,rl,4,False,True,False,False,False,False,200,200,5.228468277237507,1.3963949977594066,0,45
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| 651 |
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obstacle_shift_0.5,rl,4,False,True,False,False,False,False,200,200,4.151063236518054,1.0445408958073576,0,36
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| 652 |
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obstacle_shift_0.5,rl,4,False,False,True,True,False,False,,200,1.9593529732398813,-0.11335124199325114,3,18
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| 653 |
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obstacle_shift_0.5,rl,4,False,False,False,False,False,True,,200,19.105543438430903,0.13983591010299606,2,200
|
| 654 |
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obstacle_shift_0.5,rl,4,True,True,False,False,False,False,200,200,17.331151196360658,0.16450484458280945,0,153
|
| 655 |
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obstacle_shift_0.5,rl,4,True,False,True,True,False,False,,200,12.068571796128497,-0.040515152138543176,3,116
|
| 656 |
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obstacle_shift_0.5,rl,4,True,False,True,True,False,False,,200,14.885441039768917,-0.0368963049419917,2,138
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| 657 |
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obstacle_shift_0.5,rl,4,True,False,False,False,False,True,,200,17.003444012686998,0.06711481982166134,16,200
|
| 658 |
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obstacle_shift_0.5,rl,4,False,False,True,True,False,False,,95,1.058007778057007,-0.002351164545960849,2,11
|
| 659 |
+
obstacle_shift_0.5,rl,4,False,False,True,True,False,False,,95,5.585508843987787,-0.03164222197922206,3,95
|
| 660 |
+
obstacle_shift_0.5,rl,4,False,True,False,False,False,False,95,95,5.495179522757949,0.12042503863299414,4,51
|
| 661 |
+
obstacle_shift_0.5,rl,4,False,True,False,False,False,False,95,95,9.963473514538862,0.7274940845657449,0,84
|
| 662 |
+
obstacle_shift_0.5,rl,4,True,True,False,False,False,False,200,200,15.791912125386993,0.4452174552413716,0,133
|
| 663 |
+
obstacle_shift_0.5,rl,4,True,True,False,False,False,False,200,200,15.216409261675548,0.36895365235752464,0,134
|
| 664 |
+
obstacle_shift_0.5,rl,4,True,True,False,False,False,False,200,200,21.81595668113714,0.27744665602730123,0,200
|
| 665 |
+
obstacle_shift_0.5,rl,4,True,True,False,False,False,False,200,200,20.502352310279434,0.5289416053582761,0,190
|
| 666 |
+
obstacle_shift_0.5,rl,4,False,True,False,False,False,False,96,96,8.175312324266455,0.2315359936821284,0,75
|
| 667 |
+
obstacle_shift_0.5,rl,4,False,True,False,False,False,False,96,96,9.57664712754637,0.10758381670015549,5,86
|
| 668 |
+
obstacle_shift_0.5,rl,4,False,False,True,True,False,False,,96,4.392609751438392,-0.03158881044718875,3,38
|
| 669 |
+
obstacle_shift_0.5,rl,4,False,True,False,False,False,False,96,96,10.526270320565107,0.4269057486644712,0,96
|
| 670 |
+
obstacle_shift_0.5,rl,4,True,True,False,False,False,False,156,156,11.271993864069875,0.7198937015196887,0,95
|
| 671 |
+
obstacle_shift_0.5,rl,4,True,True,False,False,False,False,156,156,15.795800264590062,0.5462341487311638,0,134
|
| 672 |
+
obstacle_shift_0.5,rl,4,True,True,False,False,False,False,156,156,16.52239226086538,0.5146298318565456,0,147
|
| 673 |
+
obstacle_shift_0.5,rl,4,True,True,False,False,False,False,156,156,16.24208179152008,0.5146298318565456,0,156
|
| 674 |
+
obstacle_shift_0.5,rl,4,False,False,True,True,False,False,,78,2.277873954475234,-0.02728598340548638,2,20
|
| 675 |
+
obstacle_shift_0.5,rl,4,False,True,False,False,False,False,78,78,4.919165623606369,1.0490710472918368,0,42
|
| 676 |
+
obstacle_shift_0.5,rl,4,False,True,False,False,False,False,78,78,9.044400674431214,0.4796181898993467,0,78
|
| 677 |
+
obstacle_shift_0.5,rl,4,False,False,True,True,False,False,,78,4.342473076456963,-0.006357814823364588,3,40
|
| 678 |
+
obstacle_shift_0.5,rl,4,True,False,True,True,False,False,,138,10.878306969262816,-0.09080730048001273,3,92
|
| 679 |
+
obstacle_shift_0.5,rl,4,True,True,False,False,False,False,138,138,15.60508128983521,0.314455096802552,0,131
|
| 680 |
+
obstacle_shift_0.5,rl,4,True,False,True,True,False,False,,138,13.841306615096473,-0.0767399759542925,3,120
|
| 681 |
+
obstacle_shift_0.5,rl,4,True,False,True,True,False,False,,138,15.91227237687861,-0.021393688666890842,4,138
|
| 682 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,True,False,False,,200,3.3837628789279277,-0.04970012007553218,3,30
|
| 683 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,True,False,False,False,False,200,200,5.816149152406041,0.22893844995199392,0,50
|
| 684 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,True,False,False,False,False,200,200,11.055019175387057,0.6443693565328985,0,113
|
| 685 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,False,False,False,True,,200,8.61036938049069,1.4599493841971147,0,200
|
| 686 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,36,1.8621182770027862,-0.011291296455305355,8,17
|
| 687 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,36,1.862093606956414,-0.011291296455305355,8,17
|
| 688 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,36,2.8094770632758252,-0.017008667412117262,3,26
|
| 689 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,36,4.186387753240362,-0.0017693028360659113,3,36
|
| 690 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,False,False,False,True,,200,7.698532670241988,0.2975760826718543,0,200
|
| 691 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,True,False,False,,200,0.9486094369790932,-0.02584333680720885,2,9
|
| 692 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,False,False,False,True,,200,18.743354436184845,0.2338289193833032,0,200
|
| 693 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,True,False,False,,200,5.6110087572173315,-0.03181611215092939,3,48
|
| 694 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,38,3.9417053153711934,-0.057256924261575304,3,38
|
| 695 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,38,4.443298954221889,-0.057256924261575304,3,38
|
| 696 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,38,0.5957873027950719,-0.016468191310140767,2,6
|
| 697 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,38,0.6054878108104846,-0.016468191310140767,2,6
|
| 698 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,True,False,False,False,False,200,200,7.33236653798883,0.05305588773674508,8,74
|
| 699 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,True,False,False,False,False,200,200,3.9058518557260955,1.0938953542290095,0,34
|
| 700 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,False,False,False,True,,200,10.527965822762331,0.18368397048354657,0,200
|
| 701 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,False,False,False,True,,200,8.693388811532945,0.1522096820062515,0,200
|
| 702 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,38,3.0625985091787102,-0.03782727195091673,5,27
|
| 703 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,38,4.432782039735559,-0.025197555724195908,4,38
|
| 704 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,38,3.1066373942526524,-0.03782727195091673,5,27
|
| 705 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,38,1.2461217856819233,-0.021980798266644563,2,12
|
| 706 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,False,True,False,,81,2.2314414600114234,-0.00837883458817501,1,21
|
| 707 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,True,False,False,,81,5.331173329012554,-0.0018260864520697195,3,47
|
| 708 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,False,True,False,,81,2.3491891077350737,-0.00837883458817501,1,21
|
| 709 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,True,False,False,False,False,81,81,9.606364649416236,0.7938396244407024,0,81
|
| 710 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,36,3.1227058042114675,-0.07352581509012696,2,27
|
| 711 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,36,4.177630149764623,-0.019575754549964408,4,36
|
| 712 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,36,4.056980596647209,-0.023665739717126932,4,35
|
| 713 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,36,3.0599037073173463,-0.07352581509012696,2,27
|
| 714 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,True,False,False,,200,2.689669466012394,-0.002842616975464507,5,24
|
| 715 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,False,False,False,True,,200,12.980782786384177,0.03162005713417604,5,200
|
| 716 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,True,False,False,False,False,200,200,6.5398856763354365,0.7671974914153277,0,56
|
| 717 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,True,False,False,,200,10.84228902746696,-0.043132511757914216,3,92
|
| 718 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,200,4.624327388623127,-0.027076132188047275,3,40
|
| 719 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,200,1.4195666284782509,-0.06838611037349551,3,13
|
| 720 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,False,False,False,True,,200,19.98118602367645,0.23859372605463047,0,200
|
| 721 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,False,True,False,,200,1.427578406986312,-0.06838611037349551,3,13
|
| 722 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,True,False,False,,58,3.682107861253275,-0.049422039075029345,3,32
|
| 723 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,True,False,False,False,False,58,58,5.33794564230343,0.7245599970714824,0,48
|
| 724 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,False,True,False,,58,6.843638659196534,-0.08874014945856573,2,58
|
| 725 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,False,False,True,False,True,False,,58,6.27603916727396,-0.08874014945856573,2,58
|
| 726 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,40,4.083248539142992,-0.015290589124260734,2,35
|
| 727 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,40,4.65241002292351,-0.006089812812549056,2,40
|
| 728 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,40,3.818485035042883,-0.09564551376805497,11,33
|
| 729 |
+
ablation_no_neighbour_4agent_clean,rl_no_neighbour,4,True,False,True,True,False,False,,40,3.3015600691731404,-0.029672944171913684,11,29
|
evaluate.py
ADDED
|
@@ -0,0 +1,188 @@
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|
|
|
| 1 |
+
"""Deterministic evaluation: RL (greedy mean action) vs classical.
|
| 2 |
+
|
| 3 |
+
Layouts are FIXED and identical for every policy/condition:
|
| 4 |
+
single-agent: seeds 1000..1019 (10 general + 10 trap, alternating)
|
| 5 |
+
4-agent: seeds 2000..2011 (6 general + 6 trap, alternating)
|
| 6 |
+
Conditions (same noise RNG seed per layout/condition for both policies):
|
| 7 |
+
clean, obs_noise 0.05, obs_noise 0.10, disturbance 1.0 (accel noise),
|
| 8 |
+
obstacle_shift 0.5 (circles +x 0.5, segments +x 0.5, clipped to arena).
|
| 9 |
+
Ablation: 4-agent clean, RL with neighbour-observation components
|
| 10 |
+
(obs indices 11,12,13) zeroed at evaluation time.
|
| 11 |
+
Metrics per policy x condition x agent-count, aggregated over agents:
|
| 12 |
+
success_rate, collision_rate, collision_obstacle_rate,
|
| 13 |
+
collision_agent_rate, timeout_rate, mean steps-to-goal (successes),
|
| 14 |
+
mean path length, mean min clearance (successes),
|
| 15 |
+
safety_violation_rate (steps with clearance < 0.15 / active steps).
|
| 16 |
+
Trap/general breakdowns are recorded separately in results.json.
|
| 17 |
+
Outputs: results.json, eval.csv (one row per agent-episode).
|
| 18 |
+
"""
|
| 19 |
+
import argparse
|
| 20 |
+
import copy
|
| 21 |
+
import csv
|
| 22 |
+
import json
|
| 23 |
+
|
| 24 |
+
import numpy as np
|
| 25 |
+
import torch
|
| 26 |
+
|
| 27 |
+
from sim import ARENA, AGENT_R, SAFETY_MARGIN, generate_layout, NavSim
|
| 28 |
+
from policies import ActorCritic, classical_action, rl_action
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
def shifted_layout(layout, shift=0.5):
|
| 32 |
+
lay = copy.deepcopy(layout)
|
| 33 |
+
lay["circles"][:, 0] = np.clip(lay["circles"][:, 0] + shift, 0.8, 11.2)
|
| 34 |
+
seg = lay["segments"].copy()
|
| 35 |
+
seg[:, 0] = np.clip(seg[:, 0] + shift, 0.3, 11.7)
|
| 36 |
+
seg[:, 2] = np.clip(seg[:, 2] + shift, 0.3, 11.7)
|
| 37 |
+
lay["segments"] = seg
|
| 38 |
+
return lay
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
def run_episode(policy, net, layout, n_agents, obs_noise, disturbance,
|
| 42 |
+
noise_seed, zero_neighbour=False, shift=0.0):
|
| 43 |
+
if shift:
|
| 44 |
+
layout = shifted_layout(layout, shift)
|
| 45 |
+
sim = NavSim(n_agents, seed=noise_seed)
|
| 46 |
+
obs = sim.reset(layout, obs_noise=obs_noise, disturbance=disturbance)
|
| 47 |
+
while True:
|
| 48 |
+
if policy == "classical":
|
| 49 |
+
act = classical_action(obs)
|
| 50 |
+
if obs_noise > 0:
|
| 51 |
+
# classical uses the same noisy observation as RL
|
| 52 |
+
pass
|
| 53 |
+
elif policy == "rl":
|
| 54 |
+
o = obs.copy()
|
| 55 |
+
if zero_neighbour:
|
| 56 |
+
o[:, 11:14] = 0.0
|
| 57 |
+
act = rl_action(net, o, deterministic=True)
|
| 58 |
+
elif policy == "rl_no_neighbour":
|
| 59 |
+
o = obs.copy()
|
| 60 |
+
o[:, 11:14] = 0.0
|
| 61 |
+
act = rl_action(net, o, deterministic=True)
|
| 62 |
+
else:
|
| 63 |
+
raise ValueError(policy)
|
| 64 |
+
obs, _, done, _ = sim.step(act)
|
| 65 |
+
if done:
|
| 66 |
+
break
|
| 67 |
+
core = sim.core
|
| 68 |
+
rows = []
|
| 69 |
+
for a in range(n_agents):
|
| 70 |
+
kind = core.collision_kind[0][a]
|
| 71 |
+
rows.append({
|
| 72 |
+
"n_agents": n_agents,
|
| 73 |
+
"policy": policy,
|
| 74 |
+
"trap": bool(layout["trap"]),
|
| 75 |
+
"success": bool(core.success[0, a]),
|
| 76 |
+
"collision": bool(core.collision[0, a]),
|
| 77 |
+
"collision_obstacle": kind in ("obstacle", "obstacle+agent"),
|
| 78 |
+
"collision_agent": kind in ("agent", "obstacle+agent"),
|
| 79 |
+
"timeout": bool(not core.success[0, a] and not core.collision[0, a]),
|
| 80 |
+
"steps": int(core.steps[0]) if core.success[0, a] else None,
|
| 81 |
+
"steps_total": int(core.steps[0]),
|
| 82 |
+
"path_length": float(core.path_len[0, a]),
|
| 83 |
+
"min_clearance": float(core.min_clearance[0, a]),
|
| 84 |
+
"viol_steps": int(core.viol_steps[0, a]),
|
| 85 |
+
"active_steps": int(core.active_steps[0, a]),
|
| 86 |
+
})
|
| 87 |
+
return rows
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
def aggregate(rows):
|
| 91 |
+
n = len(rows)
|
| 92 |
+
succ = [r for r in rows if r["success"]]
|
| 93 |
+
return {
|
| 94 |
+
"n_agent_episodes": n,
|
| 95 |
+
"success_rate": float(np.mean([r["success"] for r in rows])),
|
| 96 |
+
"collision_rate": float(np.mean([r["collision"] for r in rows])),
|
| 97 |
+
"collision_obstacle_rate": float(np.mean([r["collision_obstacle"] for r in rows])),
|
| 98 |
+
"collision_agent_rate": float(np.mean([r["collision_agent"] for r in rows])),
|
| 99 |
+
"timeout_rate": float(np.mean([r["timeout"] for r in rows])),
|
| 100 |
+
"mean_steps_to_goal_success": float(np.mean([r["steps"] for r in succ])) if succ else None,
|
| 101 |
+
"mean_path_length": float(np.mean([r["path_length"] for r in rows])),
|
| 102 |
+
"mean_min_clearance_success": float(np.mean([r["min_clearance"] for r in succ])) if succ else None,
|
| 103 |
+
"safety_violation_rate": float(sum(r["viol_steps"] for r in rows) / max(sum(r["active_steps"] for r in rows), 1)),
|
| 104 |
+
}
|
| 105 |
+
|
| 106 |
+
|
| 107 |
+
def main():
|
| 108 |
+
ap = argparse.ArgumentParser()
|
| 109 |
+
ap.add_argument("--weights", default="ppo_policy.pt")
|
| 110 |
+
ap.add_argument("--out", default="results.json")
|
| 111 |
+
ap.add_argument("--csv", default="eval.csv")
|
| 112 |
+
args = ap.parse_args()
|
| 113 |
+
net = ActorCritic()
|
| 114 |
+
net.load_state_dict(torch.load(args.weights, map_location="cpu", weights_only=True))
|
| 115 |
+
net.eval()
|
| 116 |
+
|
| 117 |
+
conditions = [
|
| 118 |
+
("clean", dict(obs_noise=0.0, disturbance=0.0, shift=0.0)),
|
| 119 |
+
("obs_noise_0.05", dict(obs_noise=0.05, disturbance=0.0, shift=0.0)),
|
| 120 |
+
("obs_noise_0.10", dict(obs_noise=0.10, disturbance=0.0, shift=0.0)),
|
| 121 |
+
("disturbance_1.0", dict(obs_noise=0.0, disturbance=1.0, shift=0.0)),
|
| 122 |
+
("obstacle_shift_0.5", dict(obs_noise=0.0, disturbance=0.0, shift=0.5)),
|
| 123 |
+
]
|
| 124 |
+
all_rows = []
|
| 125 |
+
results = {"protocol": {
|
| 126 |
+
"single_agent_layout_seeds": list(range(1000, 1020)),
|
| 127 |
+
"four_agent_layout_seeds": list(range(2000, 2012)),
|
| 128 |
+
"layout_rule": "alternating general/trap starting with general; same layouts for every policy and condition",
|
| 129 |
+
"noise_seed_rule": "90000 + layout_index, same seed per layout/condition for both policies",
|
| 130 |
+
"weights": args.weights,
|
| 131 |
+
"max_steps": 200, "dt": 0.1, "arena": 12.0,
|
| 132 |
+
"safety_margin": SAFETY_MARGIN,
|
| 133 |
+
}, "conditions": {}}
|
| 134 |
+
|
| 135 |
+
for cond_name, cond in conditions:
|
| 136 |
+
results["conditions"][cond_name] = {}
|
| 137 |
+
for n_agents, seeds in ((1, range(1000, 1020)), (4, range(2000, 2012))):
|
| 138 |
+
layouts = []
|
| 139 |
+
for i, s in enumerate(seeds):
|
| 140 |
+
rng = np.random.default_rng(s)
|
| 141 |
+
layouts.append(generate_layout(rng, n_agents, trap=(i % 2 == 1)))
|
| 142 |
+
key = f"{n_agents}_agent"
|
| 143 |
+
results["conditions"][cond_name][key] = {}
|
| 144 |
+
for policy in ("classical", "rl"):
|
| 145 |
+
rows = []
|
| 146 |
+
for li, lay in enumerate(layouts):
|
| 147 |
+
rows.extend(run_episode(policy, net, lay, n_agents,
|
| 148 |
+
cond["obs_noise"], cond["disturbance"],
|
| 149 |
+
noise_seed=90000 + li, shift=cond["shift"]))
|
| 150 |
+
for r in rows:
|
| 151 |
+
r["condition"] = cond_name
|
| 152 |
+
all_rows.extend(rows)
|
| 153 |
+
results["conditions"][cond_name][key][policy] = aggregate(rows)
|
| 154 |
+
for trap_val, label in ((False, "general"), (True, "trap")):
|
| 155 |
+
sub = [r for r in rows if r["trap"] == trap_val]
|
| 156 |
+
results["conditions"][cond_name][key][policy][f"breakdown_{label}"] = aggregate(sub)
|
| 157 |
+
|
| 158 |
+
# ablation: 4-agent clean, RL with neighbour observation zeroed
|
| 159 |
+
abl_rows, abl_base = [], []
|
| 160 |
+
for i, s in enumerate(range(2000, 2012)):
|
| 161 |
+
rng = np.random.default_rng(s)
|
| 162 |
+
lay = generate_layout(rng, 4, trap=(i % 2 == 1))
|
| 163 |
+
abl_rows.extend(run_episode("rl_no_neighbour", net, lay, 4, 0.0, 0.0, 90000 + i))
|
| 164 |
+
abl_base.extend(run_episode("rl", net, lay, 4, 0.0, 0.0, 90000 + i))
|
| 165 |
+
for r in abl_rows:
|
| 166 |
+
r["condition"] = "ablation_no_neighbour_4agent_clean"
|
| 167 |
+
all_rows.extend(abl_rows)
|
| 168 |
+
results["ablation_no_neighbour_4agent_clean"] = {
|
| 169 |
+
"rl_with_neighbour": aggregate(abl_base),
|
| 170 |
+
"rl_no_neighbour": aggregate(abl_rows),
|
| 171 |
+
}
|
| 172 |
+
|
| 173 |
+
with open(args.out, "w") as f:
|
| 174 |
+
json.dump(results, f, indent=2)
|
| 175 |
+
with open(args.csv, "w", newline="") as f:
|
| 176 |
+
fields = ["condition", "policy", "n_agents", "trap", "success", "collision",
|
| 177 |
+
"collision_obstacle", "collision_agent", "timeout", "steps",
|
| 178 |
+
"steps_total", "path_length", "min_clearance", "viol_steps", "active_steps"]
|
| 179 |
+
w = csv.DictWriter(f, fieldnames=fields)
|
| 180 |
+
w.writeheader()
|
| 181 |
+
for r in all_rows:
|
| 182 |
+
w.writerow({k: r[k] for k in fields})
|
| 183 |
+
print(json.dumps(results["conditions"], indent=2))
|
| 184 |
+
print("ablation:", json.dumps(results["ablation_no_neighbour_4agent_clean"], indent=2))
|
| 185 |
+
|
| 186 |
+
|
| 187 |
+
if __name__ == "__main__":
|
| 188 |
+
main()
|
plots.py
ADDED
|
@@ -0,0 +1,141 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Plots from results.json + live rollouts of the trained policy.
|
| 2 |
+
|
| 3 |
+
Outputs (plots/):
|
| 4 |
+
trap_trajectories.png - one wall-trap layout, RL vs classical
|
| 5 |
+
four_agent.png - one 4-agent layout, RL vs classical (2 panels)
|
| 6 |
+
clean_comparison.png - bar chart: clean success / collision, RL vs
|
| 7 |
+
classical, single-agent and 4-agent
|
| 8 |
+
training_curve.png - training success/collision over iterations
|
| 9 |
+
"""
|
| 10 |
+
import argparse
|
| 11 |
+
import json
|
| 12 |
+
|
| 13 |
+
import matplotlib
|
| 14 |
+
matplotlib.use("Agg")
|
| 15 |
+
import matplotlib.pyplot as plt
|
| 16 |
+
import numpy as np
|
| 17 |
+
import torch
|
| 18 |
+
|
| 19 |
+
from sim import generate_layout, NavSim
|
| 20 |
+
from policies import ActorCritic, classical_action, rl_action
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
def draw(ax, layout, traj, goals, title):
|
| 24 |
+
ax.set_xlim(0, 12); ax.set_ylim(0, 12)
|
| 25 |
+
ax.set_aspect("equal"); ax.set_title(title, fontsize=10)
|
| 26 |
+
ax.set_xlabel("x"); ax.set_ylabel("y")
|
| 27 |
+
for (cx, cy, r) in layout["circles"]:
|
| 28 |
+
ax.add_patch(plt.Circle((cx, cy), r, color="0.7", zorder=1))
|
| 29 |
+
ax.add_patch(plt.Circle((cx, cy), r, fill=False, color="k", lw=0.8, zorder=2))
|
| 30 |
+
for (x1, y1, x2, y2) in layout["segments"]:
|
| 31 |
+
ax.plot([x1, x2], [y1, y2], color="k", lw=4, solid_capstyle="butt", zorder=2)
|
| 32 |
+
colors = ["#1f77b4", "#d62728", "#2ca02c", "#9467bd"]
|
| 33 |
+
for i, tr in enumerate(traj):
|
| 34 |
+
tr = np.array(tr)
|
| 35 |
+
ax.plot(tr[:, 0], tr[:, 1], color=colors[i % 4], lw=1.5, zorder=3)
|
| 36 |
+
ax.scatter([tr[0, 0]], [tr[0, 1]], marker="o", s=45, color=colors[i % 4], zorder=4)
|
| 37 |
+
ax.scatter([goals[i, 0]], [goals[i, 1]], marker="*", s=160, color=colors[i % 4],
|
| 38 |
+
edgecolor="k", linewidth=0.5, zorder=4)
|
| 39 |
+
ax.add_patch(plt.Rectangle((0, 0), 12, 12, fill=False, color="k", lw=1.2))
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
def rollout(policy, net, layout, n_agents):
|
| 43 |
+
sim = NavSim(n_agents, seed=0)
|
| 44 |
+
obs = sim.reset(layout)
|
| 45 |
+
while True:
|
| 46 |
+
act = classical_action(obs) if policy == "classical" else rl_action(net, obs, deterministic=True)
|
| 47 |
+
obs, _, done, _ = sim.step(act)
|
| 48 |
+
if done:
|
| 49 |
+
break
|
| 50 |
+
return sim.traj, sim.success, sim.collision
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
def main():
|
| 54 |
+
ap = argparse.ArgumentParser()
|
| 55 |
+
ap.add_argument("--weights", default="ppo_policy.pt")
|
| 56 |
+
ap.add_argument("--results", default="results.json")
|
| 57 |
+
ap.add_argument("--curve", default="training_curve.csv")
|
| 58 |
+
ap.add_argument("--outdir", default="plots")
|
| 59 |
+
args = ap.parse_args()
|
| 60 |
+
import os
|
| 61 |
+
os.makedirs(args.outdir, exist_ok=True)
|
| 62 |
+
net = ActorCritic()
|
| 63 |
+
net.load_state_dict(torch.load(args.weights, map_location="cpu", weights_only=True))
|
| 64 |
+
net.eval()
|
| 65 |
+
|
| 66 |
+
# trap layout: seed 1001 (second single-agent layout, trap)
|
| 67 |
+
rng = np.random.default_rng(1001)
|
| 68 |
+
trap_layout = generate_layout(rng, 1, trap=True)
|
| 69 |
+
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
|
| 70 |
+
for ax, policy in zip(axes, ["classical", "rl"]):
|
| 71 |
+
traj, succ, coll = rollout(policy, net, trap_layout, 1)
|
| 72 |
+
draw(ax, trap_layout, traj, trap_layout["goals"],
|
| 73 |
+
f"{policy}: {'SUCCESS' if succ[0] else 'COLLISION' if coll[0] else 'TIMEOUT'}")
|
| 74 |
+
fig.tight_layout()
|
| 75 |
+
fig.savefig(f"{args.outdir}/trap_trajectories.png", dpi=150)
|
| 76 |
+
plt.close(fig)
|
| 77 |
+
|
| 78 |
+
# 4-agent layout: seed 2001 (second 4-agent layout, trap)
|
| 79 |
+
rng = np.random.default_rng(2001)
|
| 80 |
+
lay4 = generate_layout(rng, 4, trap=True)
|
| 81 |
+
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
|
| 82 |
+
for ax, policy in zip(axes, ["classical", "rl"]):
|
| 83 |
+
traj, succ, coll = rollout(policy, net, lay4, 4)
|
| 84 |
+
draw(ax, lay4, traj, lay4["goals"],
|
| 85 |
+
f"{policy}: {int(succ.sum())}/4 success, {int(coll.sum())} collisions")
|
| 86 |
+
fig.tight_layout()
|
| 87 |
+
fig.savefig(f"{args.outdir}/four_agent.png", dpi=150)
|
| 88 |
+
plt.close(fig)
|
| 89 |
+
|
| 90 |
+
# bar chart from results.json
|
| 91 |
+
with open(args.results) as f:
|
| 92 |
+
res = json.load(f)
|
| 93 |
+
clean = res["conditions"]["clean"]
|
| 94 |
+
labels = ["1-agent\nclassical", "1-agent\nRL", "4-agent\nclassical", "4-agent\nRL"]
|
| 95 |
+
succ = [clean["1_agent"]["classical"]["success_rate"],
|
| 96 |
+
clean["1_agent"]["rl"]["success_rate"],
|
| 97 |
+
clean["4_agent"]["classical"]["success_rate"],
|
| 98 |
+
clean["4_agent"]["rl"]["success_rate"]]
|
| 99 |
+
coll = [clean["1_agent"]["classical"]["collision_rate"],
|
| 100 |
+
clean["1_agent"]["rl"]["collision_rate"],
|
| 101 |
+
clean["4_agent"]["classical"]["collision_rate"],
|
| 102 |
+
clean["4_agent"]["rl"]["collision_rate"]]
|
| 103 |
+
x = np.arange(len(labels)); w = 0.35
|
| 104 |
+
fig, ax = plt.subplots(figsize=(8, 4.5))
|
| 105 |
+
ax.bar(x - w / 2, succ, w, label="success rate")
|
| 106 |
+
ax.bar(x + w / 2, coll, w, label="collision rate")
|
| 107 |
+
ax.set_xticks(x); ax.set_xticklabels(labels)
|
| 108 |
+
ax.set_ylim(0, 1.05); ax.set_ylabel("rate"); ax.set_title("Clean evaluation: RL vs classical")
|
| 109 |
+
ax.legend()
|
| 110 |
+
for i, v in enumerate(succ):
|
| 111 |
+
ax.text(i - w / 2, v + 0.02, f"{v:.2f}", ha="center", fontsize=9)
|
| 112 |
+
for i, v in enumerate(coll):
|
| 113 |
+
ax.text(i + w / 2, v + 0.02, f"{v:.2f}", ha="center", fontsize=9)
|
| 114 |
+
fig.tight_layout()
|
| 115 |
+
fig.savefig(f"{args.outdir}/clean_comparison.png", dpi=150)
|
| 116 |
+
plt.close(fig)
|
| 117 |
+
|
| 118 |
+
# training curve (concatenated curves passed in --curve if present)
|
| 119 |
+
try:
|
| 120 |
+
import csv
|
| 121 |
+
iters, s, c = [], [], []
|
| 122 |
+
with open(args.curve) as f:
|
| 123 |
+
for row in csv.DictReader(f):
|
| 124 |
+
if row["success_rate_finished"] and row["success_rate_finished"] != "nan":
|
| 125 |
+
iters.append(int(row["iteration"])); s.append(float(row["success_rate_finished"]))
|
| 126 |
+
c.append(float(row["collision_rate_finished"]))
|
| 127 |
+
fig, ax = plt.subplots(figsize=(8, 4))
|
| 128 |
+
ax.plot(iters, s, label="training success rate (finished episodes)")
|
| 129 |
+
ax.plot(iters, c, label="training collision rate")
|
| 130 |
+
ax.set_xlabel("iteration"); ax.set_ylabel("rate"); ax.legend()
|
| 131 |
+
ax.set_title("PPO training curve (last training run only)")
|
| 132 |
+
fig.tight_layout()
|
| 133 |
+
fig.savefig(f"{args.outdir}/training_curve.png", dpi=150)
|
| 134 |
+
plt.close(fig)
|
| 135 |
+
except FileNotFoundError:
|
| 136 |
+
pass
|
| 137 |
+
print("plots written to", args.outdir)
|
| 138 |
+
|
| 139 |
+
|
| 140 |
+
if __name__ == "__main__":
|
| 141 |
+
main()
|
plots/clean_comparison.png
ADDED
|
plots/four_agent.png
ADDED
|
plots/training_curve.png
ADDED
|
Git LFS Details
|
plots/trap_trajectories.png
ADDED
|
policies.py
ADDED
|
@@ -0,0 +1,114 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Policies: textbook classical potential-field controller and PPO policy net.
|
| 2 |
+
|
| 3 |
+
CLASSICAL (no learning, no tangential escape trick):
|
| 4 |
+
Per agent, from its own observation only:
|
| 5 |
+
desired velocity v_des = goal_dir * VMAX * min(1, goal_dist/1.5)
|
| 6 |
+
a_attr = KD * (v_des - vel), KD = 2.0
|
| 7 |
+
repulsion from nearest circle, nearest wall segment and nearest
|
| 8 |
+
neighbour, each radial only (away from the hazard), active when the
|
| 9 |
+
surface/centre distance d < D0 = 1.5:
|
| 10 |
+
a_rep = ETA * (1/d_eff - 1/D0) / d_eff^2 * away_unit,
|
| 11 |
+
ETA = 1.5, d_eff = max(d, 0.3)
|
| 12 |
+
a = clip(a_attr + sum(a_rep), -AMAX, AMAX)
|
| 13 |
+
This is the honest textbook baseline: in the wall-trap layout the
|
| 14 |
+
rightward attraction and leftward wall repulsion cancel in the
|
| 15 |
+
middle of the wall, so the agent stalls / oscillates there.
|
| 16 |
+
|
| 17 |
+
RL: shared decentralized PPO actor (MLP 15 -> 128 -> 128, Gaussian
|
| 18 |
+
actions, tanh-bounded to AMAX). The same weights drive every agent
|
| 19 |
+
from its own observation; see train.py.
|
| 20 |
+
"""
|
| 21 |
+
import numpy as np
|
| 22 |
+
import torch
|
| 23 |
+
import torch.nn as nn
|
| 24 |
+
|
| 25 |
+
from sim import VMAX, AMAX, AGENT_R
|
| 26 |
+
|
| 27 |
+
KD = 2.0
|
| 28 |
+
ETA = 6.0
|
| 29 |
+
D0 = 2.0
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
def classical_action(obs):
|
| 33 |
+
"""obs: (..., 20) numpy. Returns (..., 2) accelerations."""
|
| 34 |
+
obs = np.asarray(obs, dtype=np.float64)
|
| 35 |
+
vel = obs[..., 0:2] * VMAX
|
| 36 |
+
goal_vec = obs[..., 2:4] * 12.0
|
| 37 |
+
goal_dist = obs[..., 4] * 12.0
|
| 38 |
+
goal_dir = goal_vec / np.maximum(goal_dist[..., None], 1e-6)
|
| 39 |
+
speed_factor = np.minimum(1.0, goal_dist / 1.5)[..., None]
|
| 40 |
+
v_des = goal_dir * VMAX * speed_factor
|
| 41 |
+
acc = KD * (v_des - vel)
|
| 42 |
+
|
| 43 |
+
def repel(dir_xy, dist_scaled, is_neighbour=False):
|
| 44 |
+
# dist_scaled is surface distance/12 for obstacles/segments,
|
| 45 |
+
# centre distance/12 for neighbours
|
| 46 |
+
d = dist_scaled * 12.0
|
| 47 |
+
if is_neighbour:
|
| 48 |
+
d_eff = np.maximum(d - 2 * AGENT_R, 0.05)
|
| 49 |
+
else:
|
| 50 |
+
d_eff = np.maximum(d + AGENT_R, 0.3)
|
| 51 |
+
mag = np.where(d < D0, ETA * (1.0 / d_eff - 1.0 / D0) / (d_eff ** 2), 0.0)
|
| 52 |
+
return mag[..., None] * dir_xy
|
| 53 |
+
|
| 54 |
+
acc = acc + repel(obs[..., 5:7], obs[..., 7])
|
| 55 |
+
acc = acc + repel(obs[..., 8:10], obs[..., 10])
|
| 56 |
+
acc = acc + repel(obs[..., 11:13], obs[..., 13], is_neighbour=True)
|
| 57 |
+
return np.clip(acc, -AMAX, AMAX).astype(np.float32)
|
| 58 |
+
|
| 59 |
+
|
| 60 |
+
class ActorCritic(nn.Module):
|
| 61 |
+
def __init__(self, obs_dim=20, hidden=128):
|
| 62 |
+
super().__init__()
|
| 63 |
+
self.actor = nn.Sequential(
|
| 64 |
+
nn.Linear(obs_dim, hidden), nn.Tanh(),
|
| 65 |
+
nn.Linear(hidden, hidden), nn.Tanh(),
|
| 66 |
+
)
|
| 67 |
+
self.mean = nn.Linear(hidden, 2)
|
| 68 |
+
# init std ~0.37 (was 0.61): gentler early exploration, fewer
|
| 69 |
+
# full-speed collisions before the policy has any signal
|
| 70 |
+
self.log_std = nn.Parameter(torch.full((2,), -1.0))
|
| 71 |
+
self.critic = nn.Sequential(
|
| 72 |
+
nn.Linear(obs_dim, hidden), nn.Tanh(),
|
| 73 |
+
nn.Linear(hidden, hidden), nn.Tanh(),
|
| 74 |
+
nn.Linear(hidden, 1),
|
| 75 |
+
)
|
| 76 |
+
|
| 77 |
+
def forward(self, obs):
|
| 78 |
+
h = self.actor(obs)
|
| 79 |
+
return self.mean(h), self.critic(obs).squeeze(-1)
|
| 80 |
+
|
| 81 |
+
def act(self, obs, deterministic=False):
|
| 82 |
+
"""obs: (..., 20) torch tensor. Returns action in [-AMAX, AMAX],
|
| 83 |
+
logprob of the pre-tanh Gaussian sample, and value."""
|
| 84 |
+
mean, value = self.forward(obs)
|
| 85 |
+
std = self.log_std.exp().expand_as(mean)
|
| 86 |
+
if deterministic:
|
| 87 |
+
raw = mean
|
| 88 |
+
logp = torch.zeros(mean.shape[:-1], device=mean.device)
|
| 89 |
+
else:
|
| 90 |
+
dist = torch.distributions.Normal(mean, std)
|
| 91 |
+
raw = dist.rsample()
|
| 92 |
+
logp = dist.log_prob(raw).sum(-1)
|
| 93 |
+
action = torch.tanh(raw) * AMAX
|
| 94 |
+
return action, logp, value
|
| 95 |
+
|
| 96 |
+
def evaluate(self, obs, raw_action):
|
| 97 |
+
mean, value = self.forward(obs)
|
| 98 |
+
std = self.log_std.exp().expand_as(mean)
|
| 99 |
+
dist = torch.distributions.Normal(mean, std)
|
| 100 |
+
return dist.log_prob(raw_action).sum(-1), dist.entropy().sum(-1), value
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
def load_policy(path, device="cpu"):
|
| 104 |
+
net = ActorCritic()
|
| 105 |
+
net.load_state_dict(torch.load(path, map_location=device, weights_only=True))
|
| 106 |
+
net.eval()
|
| 107 |
+
return net
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
@torch.no_grad()
|
| 111 |
+
def rl_action(net, obs_np, deterministic=True):
|
| 112 |
+
obs = torch.as_tensor(np.asarray(obs_np, dtype=np.float32))
|
| 113 |
+
action, _, _ = net.act(obs, deterministic=deterministic)
|
| 114 |
+
return action.numpy()
|
ppo_policy.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7de18d1b1eecbb0e32341948dddc57081ca4a174d68a2bd67d743535f19d078c
|
| 3 |
+
size 160245
|
results.json
ADDED
|
@@ -0,0 +1,825 @@
|
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|
|
| 1 |
+
{
|
| 2 |
+
"protocol": {
|
| 3 |
+
"single_agent_layout_seeds": [
|
| 4 |
+
1000,
|
| 5 |
+
1001,
|
| 6 |
+
1002,
|
| 7 |
+
1003,
|
| 8 |
+
1004,
|
| 9 |
+
1005,
|
| 10 |
+
1006,
|
| 11 |
+
1007,
|
| 12 |
+
1008,
|
| 13 |
+
1009,
|
| 14 |
+
1010,
|
| 15 |
+
1011,
|
| 16 |
+
1012,
|
| 17 |
+
1013,
|
| 18 |
+
1014,
|
| 19 |
+
1015,
|
| 20 |
+
1016,
|
| 21 |
+
1017,
|
| 22 |
+
1018,
|
| 23 |
+
1019
|
| 24 |
+
],
|
| 25 |
+
"four_agent_layout_seeds": [
|
| 26 |
+
2000,
|
| 27 |
+
2001,
|
| 28 |
+
2002,
|
| 29 |
+
2003,
|
| 30 |
+
2004,
|
| 31 |
+
2005,
|
| 32 |
+
2006,
|
| 33 |
+
2007,
|
| 34 |
+
2008,
|
| 35 |
+
2009,
|
| 36 |
+
2010,
|
| 37 |
+
2011
|
| 38 |
+
],
|
| 39 |
+
"layout_rule": "alternating general/trap starting with general; same layouts for every policy and condition",
|
| 40 |
+
"noise_seed_rule": "90000 + layout_index, same seed per layout/condition for both policies",
|
| 41 |
+
"weights": "ppo_policy.pt",
|
| 42 |
+
"max_steps": 200,
|
| 43 |
+
"dt": 0.1,
|
| 44 |
+
"arena": 12.0,
|
| 45 |
+
"safety_margin": 0.15
|
| 46 |
+
},
|
| 47 |
+
"conditions": {
|
| 48 |
+
"clean": {
|
| 49 |
+
"1_agent": {
|
| 50 |
+
"classical": {
|
| 51 |
+
"n_agent_episodes": 20,
|
| 52 |
+
"success_rate": 0.45,
|
| 53 |
+
"collision_rate": 0.0,
|
| 54 |
+
"collision_obstacle_rate": 0.0,
|
| 55 |
+
"collision_agent_rate": 0.0,
|
| 56 |
+
"timeout_rate": 0.55,
|
| 57 |
+
"mean_steps_to_goal_success": 73.22222222222223,
|
| 58 |
+
"mean_path_length": 5.3478555207171485,
|
| 59 |
+
"mean_min_clearance_success": 0.588437716376736,
|
| 60 |
+
"safety_violation_rate": 0.0,
|
| 61 |
+
"breakdown_general": {
|
| 62 |
+
"n_agent_episodes": 10,
|
| 63 |
+
"success_rate": 0.9,
|
| 64 |
+
"collision_rate": 0.0,
|
| 65 |
+
"collision_obstacle_rate": 0.0,
|
| 66 |
+
"collision_agent_rate": 0.0,
|
| 67 |
+
"timeout_rate": 0.1,
|
| 68 |
+
"mean_steps_to_goal_success": 73.22222222222223,
|
| 69 |
+
"mean_path_length": 6.907327148951262,
|
| 70 |
+
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|
| 71 |
+
"safety_violation_rate": 0.0
|
| 72 |
+
},
|
| 73 |
+
"breakdown_trap": {
|
| 74 |
+
"n_agent_episodes": 10,
|
| 75 |
+
"success_rate": 0.0,
|
| 76 |
+
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|
| 77 |
+
"collision_obstacle_rate": 0.0,
|
| 78 |
+
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|
| 79 |
+
"timeout_rate": 1.0,
|
| 80 |
+
"mean_steps_to_goal_success": null,
|
| 81 |
+
"mean_path_length": 3.788383892483034,
|
| 82 |
+
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|
| 83 |
+
"safety_violation_rate": 0.0
|
| 84 |
+
}
|
| 85 |
+
},
|
| 86 |
+
"rl": {
|
| 87 |
+
"n_agent_episodes": 20,
|
| 88 |
+
"success_rate": 0.7,
|
| 89 |
+
"collision_rate": 0.2,
|
| 90 |
+
"collision_obstacle_rate": 0.2,
|
| 91 |
+
"collision_agent_rate": 0.0,
|
| 92 |
+
"timeout_rate": 0.1,
|
| 93 |
+
"mean_steps_to_goal_success": 86.0,
|
| 94 |
+
"mean_path_length": 9.587788908089728,
|
| 95 |
+
"mean_min_clearance_success": 0.41860608828737567,
|
| 96 |
+
"safety_violation_rate": 0.03286118980169972,
|
| 97 |
+
"breakdown_general": {
|
| 98 |
+
"n_agent_episodes": 10,
|
| 99 |
+
"success_rate": 0.6,
|
| 100 |
+
"collision_rate": 0.3,
|
| 101 |
+
"collision_obstacle_rate": 0.3,
|
| 102 |
+
"collision_agent_rate": 0.0,
|
| 103 |
+
"timeout_rate": 0.1,
|
| 104 |
+
"mean_steps_to_goal_success": 61.166666666666664,
|
| 105 |
+
"mean_path_length": 7.149988021333305,
|
| 106 |
+
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|
| 107 |
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"safety_violation_rate": 0.07430340557275542
|
| 108 |
+
},
|
| 109 |
+
"breakdown_trap": {
|
| 110 |
+
"n_agent_episodes": 10,
|
| 111 |
+
"success_rate": 0.8,
|
| 112 |
+
"collision_rate": 0.1,
|
| 113 |
+
"collision_obstacle_rate": 0.1,
|
| 114 |
+
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|
| 115 |
+
"timeout_rate": 0.1,
|
| 116 |
+
"mean_steps_to_goal_success": 104.625,
|
| 117 |
+
"mean_path_length": 12.025589794846148,
|
| 118 |
+
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|
| 119 |
+
"safety_violation_rate": 0.008936550491510277
|
| 120 |
+
}
|
| 121 |
+
}
|
| 122 |
+
},
|
| 123 |
+
"4_agent": {
|
| 124 |
+
"classical": {
|
| 125 |
+
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|
| 126 |
+
"success_rate": 0.3958333333333333,
|
| 127 |
+
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
+
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
+
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
+
},
|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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}
|
| 159 |
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},
|
| 160 |
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"rl": {
|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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"mean_steps_to_goal_success": 102.61111111111111,
|
| 179 |
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|
| 180 |
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|
| 181 |
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|
| 182 |
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},
|
| 183 |
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|
| 184 |
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|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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|
| 189 |
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"timeout_rate": 0.08333333333333333,
|
| 190 |
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"mean_steps_to_goal_success": 175.9,
|
| 191 |
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"mean_path_length": 14.850280003684333,
|
| 192 |
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|
| 193 |
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|
| 194 |
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}
|
| 195 |
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}
|
| 196 |
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}
|
| 197 |
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},
|
| 198 |
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"obs_noise_0.05": {
|
| 199 |
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| 798 |
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| 817 |
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| 823 |
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|
| 824 |
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}
|
| 825 |
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}
|
sim.py
ADDED
|
@@ -0,0 +1,441 @@
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|
| 1 |
+
"""Deterministic 2D multi-agent navigation simulator.
|
| 2 |
+
|
| 3 |
+
Arena 12x12 with walls. Agents are double-integrators:
|
| 4 |
+
v += a*dt, |v| <= VMAX, pos += v*dt, |a| <= AMAX, dt = 0.1.
|
| 5 |
+
Agent radius 0.25. Collision with a circle obstacle, a wall segment,
|
| 6 |
+
an arena wall, or another agent ends that agent's episode as a failure.
|
| 7 |
+
Success = goal reached (dist < 0.4) with no collision in the episode.
|
| 8 |
+
|
| 9 |
+
Observations are decentralized and shared-policy: each agent sees only
|
| 10 |
+
its own velocity, relative goal, nearest circle obstacle, nearest wall
|
| 11 |
+
segment, nearest other agent, and nearest arena-wall distance.
|
| 12 |
+
OBS layout (15 dims, see OBS_NAMES):
|
| 13 |
+
0,1 own velocity / VMAX
|
| 14 |
+
2,3 relative goal vector / 12
|
| 15 |
+
4 goal distance / 12
|
| 16 |
+
5,6 unit vector away from nearest circle obstacle (surface point)
|
| 17 |
+
7 surface distance to nearest circle obstacle / 12
|
| 18 |
+
8,9 unit vector away from nearest wall-segment point
|
| 19 |
+
10 distance to nearest wall segment / 12
|
| 20 |
+
11,12 unit vector away from nearest other agent
|
| 21 |
+
13 centre distance to nearest other agent / 12 (1.0 if no other agent)
|
| 22 |
+
14 distance to nearest arena wall / 12
|
| 23 |
+
15,16 relative vector to wall-segment endpoint 1 / 12
|
| 24 |
+
17,18 relative vector to wall-segment endpoint 2 / 12
|
| 25 |
+
19 radius of nearest circle obstacle (0.5-0.8)
|
| 26 |
+
|
| 27 |
+
Dims 15-19 were added after a failed training run: with only the
|
| 28 |
+
nearest segment point, the policy could not observe where the wall
|
| 29 |
+
ends, so choosing a gap was unobservable and the wall-trap detour was
|
| 30 |
+
unlearnable. Endpoint vectors are standard line-feature sensing
|
| 31 |
+
(e.g. a lidar return's endpoints). They are appended at the end so
|
| 32 |
+
the classical controller's indices 0-14 are unchanged.
|
| 33 |
+
|
| 34 |
+
The batched core (BatchedSim) is the single source of physics; NavSim
|
| 35 |
+
wraps it with n_envs=1 and records trajectories for plotting/eval.
|
| 36 |
+
"""
|
| 37 |
+
import numpy as np
|
| 38 |
+
|
| 39 |
+
ARENA = 12.0
|
| 40 |
+
DT = 0.1
|
| 41 |
+
# Limits chosen so the control problem is learnable: at VMAX 2.0 the
|
| 42 |
+
# stopping distance (v^2/2a = 0.5 m) barely fit inside the 0.8 m
|
| 43 |
+
# safety zone, so exploration noise alone collided near hazards.
|
| 44 |
+
# At 1.2/3.0 the stopping distance is 0.24 m. Changed before the
|
| 45 |
+
# successful training run; classical gains were re-checked after.
|
| 46 |
+
VMAX = 1.2
|
| 47 |
+
AMAX = 3.0
|
| 48 |
+
AGENT_R = 0.25
|
| 49 |
+
GOAL_TOL = 0.4
|
| 50 |
+
MAX_STEPS = 200
|
| 51 |
+
OBS_DIM = 20
|
| 52 |
+
OBS_NAMES = [
|
| 53 |
+
"vel_x", "vel_y", "goal_dx", "goal_dy", "goal_dist",
|
| 54 |
+
"obs_dir_x", "obs_dir_y", "obs_dist",
|
| 55 |
+
"seg_dir_x", "seg_dir_y", "seg_dist",
|
| 56 |
+
"neigh_dir_x", "neigh_dir_y", "neigh_dist", "wall_dist",
|
| 57 |
+
"seg_end1_dx", "seg_end1_dy", "seg_end2_dx", "seg_end2_dy", "obs_radius",
|
| 58 |
+
]
|
| 59 |
+
SAFETY_MARGIN = 0.15 # clearance below this counts as a safety violation
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
def _seg_closest(px, py, x1, y1, x2, y2):
|
| 63 |
+
"""Closest point on segment to (px,py); vectorised over leading axes."""
|
| 64 |
+
dx, dy = x2 - x1, y2 - y1
|
| 65 |
+
L2 = dx * dx + dy * dy
|
| 66 |
+
t = np.where(L2 > 0, ((px - x1) * dx + (py - y1) * dy) / np.maximum(L2, 1e-9), 0.0)
|
| 67 |
+
t = np.clip(t, 0.0, 1.0)
|
| 68 |
+
cx, cy = x1 + t * dx, y1 + t * dy
|
| 69 |
+
return cx, cy
|
| 70 |
+
|
| 71 |
+
|
| 72 |
+
def generate_layout(rng, n_agents, trap=None):
|
| 73 |
+
"""Seeded layout. trap=True forces the vertical wall-trap layout.
|
| 74 |
+
|
| 75 |
+
Trap: vertical segment x=6, y in [2,10]; gaps only at y<2 and y>10.
|
| 76 |
+
Starts on the left, goals on the right, so a direct radial potential
|
| 77 |
+
field is attracted right and repelled left by the wall in the middle.
|
| 78 |
+
"""
|
| 79 |
+
if trap is None:
|
| 80 |
+
trap = bool(rng.random() < 0.5)
|
| 81 |
+
attempts = 0
|
| 82 |
+
while True:
|
| 83 |
+
attempts += 1
|
| 84 |
+
if attempts > 500:
|
| 85 |
+
raise RuntimeError("layout generation failed")
|
| 86 |
+
if trap:
|
| 87 |
+
segments = np.array([[6.0, 2.0, 6.0, 10.0]], dtype=np.float64)
|
| 88 |
+
# Goals near-directly opposite the starts, mid-wall, so the
|
| 89 |
+
# direct route meets the wall head-on: the textbook local
|
| 90 |
+
# minimum. (A large y-offset would let the field slide
|
| 91 |
+
# along the wall to a gap and would not be a trap at all.)
|
| 92 |
+
ys = np.linspace(4.5, 7.5, n_agents) + rng.uniform(-0.3, 0.3, size=n_agents)
|
| 93 |
+
ys = np.clip(ys, 4.0, 8.0)
|
| 94 |
+
starts = np.stack([rng.uniform(1.0, 3.0, size=n_agents), ys], axis=1)
|
| 95 |
+
perm = rng.permutation(n_agents)
|
| 96 |
+
goal_ys = ys[perm] + rng.uniform(-0.4, 0.4, size=n_agents)
|
| 97 |
+
goals = np.stack([rng.uniform(9.0, 11.0, size=n_agents),
|
| 98 |
+
np.clip(goal_ys, 1.2, 10.8)], axis=1)
|
| 99 |
+
circles = []
|
| 100 |
+
while len(circles) < 3:
|
| 101 |
+
cx = rng.uniform(0.8, 11.2)
|
| 102 |
+
# keep circles off the wall-trap corridor so the wall
|
| 103 |
+
# itself, not a circle, is what blocks the direct route
|
| 104 |
+
if 4.8 < cx < 7.2:
|
| 105 |
+
continue
|
| 106 |
+
cy = rng.uniform(0.8, 11.2)
|
| 107 |
+
r = rng.uniform(0.5, 0.8)
|
| 108 |
+
cand = np.array([cx, cy, r])
|
| 109 |
+
if _layout_ok(starts, goals, circles, cand):
|
| 110 |
+
circles.append(cand)
|
| 111 |
+
circles = np.array(circles, dtype=np.float64)
|
| 112 |
+
else:
|
| 113 |
+
# one shorter barrier segment, placed mid-arena at a random
|
| 114 |
+
# angle, plus 3 circles anywhere (with separation checks)
|
| 115 |
+
ang = rng.uniform(0, np.pi)
|
| 116 |
+
cx0, cy0 = rng.uniform(4.0, 8.0), rng.uniform(4.0, 8.0)
|
| 117 |
+
half = 1.5
|
| 118 |
+
segments = np.array([[
|
| 119 |
+
cx0 - half * np.cos(ang), cy0 - half * np.sin(ang),
|
| 120 |
+
cx0 + half * np.cos(ang), cy0 + half * np.sin(ang),
|
| 121 |
+
]], dtype=np.float64)
|
| 122 |
+
starts = _spread_points(rng, n_agents, lo=0.8, hi=11.2)
|
| 123 |
+
goals = _spread_points(rng, n_agents, lo=0.8, hi=11.2)
|
| 124 |
+
# matched start->goal distances must be meaningful trips
|
| 125 |
+
if np.min(np.linalg.norm(starts - goals, axis=1)) < 4.0:
|
| 126 |
+
continue
|
| 127 |
+
# starts/goals must not spawn on/inside the barrier segment
|
| 128 |
+
pts = np.vstack([starts, goals])
|
| 129 |
+
cx, cy = _seg_closest(pts[:, 0], pts[:, 1], segments[0, 0], segments[0, 1],
|
| 130 |
+
segments[0, 2], segments[0, 3])
|
| 131 |
+
if np.min(np.hypot(pts[:, 0] - cx, pts[:, 1] - cy)) < 1.0:
|
| 132 |
+
continue
|
| 133 |
+
circles = []
|
| 134 |
+
while len(circles) < 3:
|
| 135 |
+
cand = np.array([rng.uniform(0.8, 11.2), rng.uniform(0.8, 11.2),
|
| 136 |
+
rng.uniform(0.5, 0.8)])
|
| 137 |
+
if _layout_ok(starts, goals, circles, cand, seg=segments[0]):
|
| 138 |
+
circles.append(cand)
|
| 139 |
+
circles = np.array(circles, dtype=np.float64)
|
| 140 |
+
return {"starts": starts, "goals": goals, "circles": circles,
|
| 141 |
+
"segments": segments, "trap": bool(trap)}
|
| 142 |
+
|
| 143 |
+
|
| 144 |
+
def _spread_points(rng, n, lo, hi):
|
| 145 |
+
pts = []
|
| 146 |
+
while len(pts) < n:
|
| 147 |
+
cand = rng.uniform(lo, hi, size=2)
|
| 148 |
+
if all(np.linalg.norm(cand - p) > 1.2 for p in pts):
|
| 149 |
+
pts.append(cand)
|
| 150 |
+
return np.array(pts, dtype=np.float64)
|
| 151 |
+
|
| 152 |
+
|
| 153 |
+
def _layout_ok(starts, goals, circles, cand, seg=None):
|
| 154 |
+
for p in np.vstack([starts, goals]):
|
| 155 |
+
if np.linalg.norm(cand[:2] - p) < cand[2] + 0.9:
|
| 156 |
+
return False
|
| 157 |
+
for c in circles:
|
| 158 |
+
if np.linalg.norm(cand[:2] - c[:2]) < cand[2] + c[2] + 0.6:
|
| 159 |
+
return False
|
| 160 |
+
if seg is not None:
|
| 161 |
+
cx, cy = _seg_closest(cand[0], cand[1], seg[0], seg[1], seg[2], seg[3])
|
| 162 |
+
if np.hypot(cand[0] - cx, cand[1] - cy) < cand[2] + 0.5:
|
| 163 |
+
return False
|
| 164 |
+
return True
|
| 165 |
+
|
| 166 |
+
|
| 167 |
+
class BatchedSim:
|
| 168 |
+
"""Vectorised (n_envs, n_agents) simulator. All physics lives here."""
|
| 169 |
+
|
| 170 |
+
def __init__(self, n_envs, n_agents, seed=0):
|
| 171 |
+
self.E, self.A = n_envs, n_agents
|
| 172 |
+
self.rng = np.random.default_rng(seed)
|
| 173 |
+
self.pos = np.zeros((n_envs, n_agents, 2))
|
| 174 |
+
self.vel = np.zeros((n_envs, n_agents, 2))
|
| 175 |
+
self.goals = np.zeros((n_envs, n_agents, 2))
|
| 176 |
+
self.circles = np.zeros((n_envs, 3, 3))
|
| 177 |
+
self.segments = np.zeros((n_envs, 1, 4))
|
| 178 |
+
self.active = np.ones((n_envs, n_agents), dtype=bool)
|
| 179 |
+
self.done_agent = np.zeros((n_envs, n_agents), dtype=bool)
|
| 180 |
+
self.success = np.zeros((n_envs, n_agents), dtype=bool)
|
| 181 |
+
self.collision = np.zeros((n_envs, n_agents), dtype=bool)
|
| 182 |
+
self.collision_kind = [["" for _ in range(n_agents)] for _ in range(n_envs)]
|
| 183 |
+
self.steps = np.zeros(n_envs, dtype=int)
|
| 184 |
+
self.prev_goal_dist = np.zeros((n_envs, n_agents))
|
| 185 |
+
self.path_len = np.zeros((n_envs, n_agents))
|
| 186 |
+
self.min_clearance = np.full((n_envs, n_agents), np.inf)
|
| 187 |
+
self.viol_steps = np.zeros((n_envs, n_agents), dtype=int)
|
| 188 |
+
self.active_steps = np.zeros((n_envs, n_agents), dtype=int)
|
| 189 |
+
|
| 190 |
+
# ---- layout / reset -------------------------------------------------
|
| 191 |
+
def reset(self, layouts, obs_noise=0.0, disturbance=0.0):
|
| 192 |
+
self.obs_noise = float(obs_noise)
|
| 193 |
+
self.disturbance = float(disturbance)
|
| 194 |
+
for e, lay in enumerate(layouts):
|
| 195 |
+
self.pos[e] = lay["starts"]
|
| 196 |
+
self.goals[e] = lay["goals"]
|
| 197 |
+
self.circles[e] = lay["circles"]
|
| 198 |
+
self.segments[e] = lay["segments"]
|
| 199 |
+
self.vel[:] = 0.0
|
| 200 |
+
self.active[:] = True
|
| 201 |
+
self.done_agent[:] = False
|
| 202 |
+
self.success[:] = False
|
| 203 |
+
self.collision[:] = False
|
| 204 |
+
self.collision_kind = [["" for _ in range(self.A)] for _ in range(self.E)]
|
| 205 |
+
self.steps[:] = 0
|
| 206 |
+
self.path_len[:] = 0.0
|
| 207 |
+
self.min_clearance[:] = np.inf
|
| 208 |
+
self.viol_steps[:] = 0
|
| 209 |
+
self.active_steps[:] = 0
|
| 210 |
+
self.prev_goal_dist = np.linalg.norm(self.goals - self.pos, axis=2)
|
| 211 |
+
self._update_clearance()
|
| 212 |
+
return self._obs()
|
| 213 |
+
|
| 214 |
+
def reset_envs(self, idxs, layouts):
|
| 215 |
+
"""Reset only the listed envs (used by training partial resets)."""
|
| 216 |
+
for e, lay in zip(idxs, layouts):
|
| 217 |
+
self.pos[e] = lay["starts"]
|
| 218 |
+
self.goals[e] = lay["goals"]
|
| 219 |
+
self.circles[e] = lay["circles"]
|
| 220 |
+
self.segments[e] = lay["segments"]
|
| 221 |
+
self.vel[e] = 0.0
|
| 222 |
+
self.active[e] = True
|
| 223 |
+
self.done_agent[e] = False
|
| 224 |
+
self.success[e] = False
|
| 225 |
+
self.collision[e] = False
|
| 226 |
+
self.collision_kind[e] = ["" for _ in range(self.A)]
|
| 227 |
+
self.steps[e] = 0
|
| 228 |
+
self.path_len[e] = 0.0
|
| 229 |
+
self.min_clearance[e] = np.inf
|
| 230 |
+
self.viol_steps[e] = 0
|
| 231 |
+
self.active_steps[e] = 0
|
| 232 |
+
self.prev_goal_dist[e] = np.linalg.norm(self.goals[e] - self.pos[e], axis=1)
|
| 233 |
+
cl = self._clearance()
|
| 234 |
+
self.min_clearance[idxs] = cl[idxs]
|
| 235 |
+
self.viol_steps[idxs] += (cl[idxs] < SAFETY_MARGIN).astype(int)
|
| 236 |
+
return self._obs()
|
| 237 |
+
|
| 238 |
+
# ---- geometry helpers ----------------------------------------------
|
| 239 |
+
def _clearance(self):
|
| 240 |
+
"""Per-agent clearance: min(surface distance) to any hazard."""
|
| 241 |
+
E, A = self.E, self.A
|
| 242 |
+
# circles: centre dist - r_circle - AGENT_R
|
| 243 |
+
d_c = np.linalg.norm(self.pos[:, :, None, :] - self.circles[:, None, :, :2], axis=3) \
|
| 244 |
+
- self.circles[:, None, :, 2] - AGENT_R # (E,A,C)
|
| 245 |
+
# segments: point-segment dist - AGENT_R
|
| 246 |
+
seg = self.segments # (E,1,4)
|
| 247 |
+
px = self.pos[:, :, None, 0]
|
| 248 |
+
py = self.pos[:, :, None, 1]
|
| 249 |
+
cx, cy = _seg_closest(px, py, seg[:, :, 0][:, :, None], seg[:, :, 1][:, :, None], seg[:, :, 2][:, :, None], seg[:, :, 3][:, :, None])
|
| 250 |
+
d_s = np.hypot(px - cx, py - cy) - AGENT_R # (E,A,1)
|
| 251 |
+
# arena walls
|
| 252 |
+
d_w = np.minimum(np.minimum(self.pos[..., 0], ARENA - self.pos[..., 0]),
|
| 253 |
+
np.minimum(self.pos[..., 1], ARENA - self.pos[..., 1])) - AGENT_R
|
| 254 |
+
# other agents: centre dist - 2*AGENT_R (ignore done agents)
|
| 255 |
+
d_pair = np.linalg.norm(self.pos[:, :, None, :] - self.pos[:, None, :, :], axis=3) - 2 * AGENT_R
|
| 256 |
+
eye = np.eye(A, dtype=bool)
|
| 257 |
+
d_pair[:, eye] = np.inf
|
| 258 |
+
d_pair = np.where(self.active[:, None, :] | self.active[:, :, None], d_pair, np.inf)
|
| 259 |
+
# done agents are not hazards for the remaining agents
|
| 260 |
+
d_pair = np.where(self.done_agent[:, None, :], np.inf, d_pair)
|
| 261 |
+
d_pair[:, eye] = np.inf
|
| 262 |
+
return np.minimum(np.minimum(d_c.min(axis=2), d_s[:, :, 0]),
|
| 263 |
+
np.minimum(d_w, d_pair.min(axis=2)))
|
| 264 |
+
|
| 265 |
+
def _update_clearance(self):
|
| 266 |
+
cl = self._clearance()
|
| 267 |
+
act = self.active
|
| 268 |
+
self.min_clearance = np.where(act, np.minimum(self.min_clearance, cl), self.min_clearance)
|
| 269 |
+
self.viol_steps += np.where(act & (cl < SAFETY_MARGIN), 1, 0)
|
| 270 |
+
|
| 271 |
+
def _obs(self):
|
| 272 |
+
E, A = self.E, self.A
|
| 273 |
+
obs = np.zeros((E, A, OBS_DIM), dtype=np.float64)
|
| 274 |
+
obs[:, :, 0:2] = self.vel / VMAX
|
| 275 |
+
rel = self.goals - self.pos
|
| 276 |
+
obs[:, :, 2:4] = rel / ARENA
|
| 277 |
+
obs[:, :, 4] = np.linalg.norm(rel, axis=2) / ARENA
|
| 278 |
+
# nearest circle (surface point)
|
| 279 |
+
d_c = np.linalg.norm(self.pos[:, :, None, :] - self.circles[:, None, :, :2], axis=3) # centre dist
|
| 280 |
+
idx = np.argmin(d_c - self.circles[:, None, :, 2], axis=2) # nearest by surface dist
|
| 281 |
+
surf = np.take_along_axis(d_c, idx[:, :, None], axis=2)[:, :, 0] \
|
| 282 |
+
- self.circles[np.arange(E)[:, None], idx, 2]
|
| 283 |
+
centres = self.circles[np.arange(E)[:, None], idx][:, :, :2] # (E,A,2)
|
| 284 |
+
away = self.pos - centres
|
| 285 |
+
nrm = np.maximum(np.linalg.norm(away, axis=2, keepdims=True), 1e-6)
|
| 286 |
+
obs[:, :, 5:7] = away / nrm
|
| 287 |
+
obs[:, :, 7] = np.clip(surf, 0.0, ARENA) / ARENA
|
| 288 |
+
# nearest segment point
|
| 289 |
+
seg = self.segments
|
| 290 |
+
px = self.pos[:, :, None, 0]
|
| 291 |
+
py = self.pos[:, :, None, 1]
|
| 292 |
+
cx, cy = _seg_closest(px, py, seg[:, :, 0][:, :, None], seg[:, :, 1][:, :, None], seg[:, :, 2][:, :, None], seg[:, :, 3][:, :, None])
|
| 293 |
+
d_seg = np.hypot(px - cx, py - cy)[:, :, 0]
|
| 294 |
+
away_s = np.stack([self.pos[:, :, 0] - cx[:, :, 0], self.pos[:, :, 1] - cy[:, :, 0]], axis=2)
|
| 295 |
+
nrm_s = np.maximum(np.linalg.norm(away_s, axis=2, keepdims=True), 1e-6)
|
| 296 |
+
obs[:, :, 8:10] = away_s / nrm_s
|
| 297 |
+
obs[:, :, 10] = d_seg / ARENA
|
| 298 |
+
# nearest other agent
|
| 299 |
+
d_pair = np.linalg.norm(self.pos[:, :, None, :] - self.pos[:, None, :, :], axis=3)
|
| 300 |
+
d_pair[:, np.eye(A, dtype=bool)] = np.inf
|
| 301 |
+
d_pair = np.where(self.done_agent[:, None, :], np.inf, d_pair)
|
| 302 |
+
d_pair[:, np.eye(A, dtype=bool)] = np.inf
|
| 303 |
+
j = np.argmin(d_pair, axis=2)
|
| 304 |
+
neigh = self.pos[np.arange(E)[:, None], j] # (E,A,2)
|
| 305 |
+
away_n = self.pos - neigh
|
| 306 |
+
nrm_n = np.maximum(np.linalg.norm(away_n, axis=2, keepdims=True), 1e-6)
|
| 307 |
+
has_neigh = np.isfinite(d_pair.min(axis=2))
|
| 308 |
+
obs[:, :, 11:13] = np.where(has_neigh[:, :, None], away_n / nrm_n, 0.0)
|
| 309 |
+
obs[:, :, 13] = np.where(has_neigh, np.minimum(d_pair.min(axis=2), ARENA), ARENA) / ARENA
|
| 310 |
+
# arena wall distance
|
| 311 |
+
obs[:, :, 14] = np.minimum(np.minimum(self.pos[..., 0], ARENA - self.pos[..., 0]),
|
| 312 |
+
np.minimum(self.pos[..., 1], ARENA - self.pos[..., 1])) / ARENA
|
| 313 |
+
# segment endpoints (relative vectors) + nearest circle radius
|
| 314 |
+
obs[:, :, 15:17] = (seg[:, :, :2] - self.pos) / ARENA
|
| 315 |
+
obs[:, :, 17:19] = (seg[:, :, 2:4] - self.pos) / ARENA
|
| 316 |
+
obs[:, :, 19] = self.circles[np.arange(E)[:, None], idx, 2]
|
| 317 |
+
if self.obs_noise > 0:
|
| 318 |
+
obs = obs + self.rng.normal(0.0, self.obs_noise, size=obs.shape)
|
| 319 |
+
obs[:, :, 0:2] = np.clip(obs[:, :, 0:2], -1.5, 1.5)
|
| 320 |
+
return obs.astype(np.float32)
|
| 321 |
+
|
| 322 |
+
# ---- step ------------------------------------------------------------
|
| 323 |
+
def step(self, actions):
|
| 324 |
+
"""actions: (E,A,2) accelerations, clipped to AMAX. Returns
|
| 325 |
+
obs, reward, episode_done (E,), info dict. Per-agent done flags
|
| 326 |
+
are in self.done_agent; reward is the training shaping reward."""
|
| 327 |
+
actions = np.clip(np.asarray(actions, dtype=np.float64), -AMAX, AMAX)
|
| 328 |
+
if self.disturbance > 0:
|
| 329 |
+
actions = actions + self.rng.normal(0.0, self.disturbance, size=actions.shape)
|
| 330 |
+
actions = np.clip(actions, -AMAX, AMAX)
|
| 331 |
+
act = self.active
|
| 332 |
+
old_pos = self.pos.copy()
|
| 333 |
+
self.vel = np.where(act[:, :, None], self.vel + actions * DT, self.vel)
|
| 334 |
+
spd = np.linalg.norm(self.vel, axis=2, keepdims=True)
|
| 335 |
+
self.vel = np.where(spd > VMAX, self.vel * (VMAX / np.maximum(spd, 1e-9)), self.vel)
|
| 336 |
+
self.pos = np.where(act[:, :, None], self.pos + self.vel * DT, self.pos)
|
| 337 |
+
self.path_len += np.where(act, np.linalg.norm(self.pos - old_pos, axis=2), 0.0)
|
| 338 |
+
self.active_steps += act.astype(int)
|
| 339 |
+
self.steps += 1
|
| 340 |
+
|
| 341 |
+
cl = self._clearance()
|
| 342 |
+
self.min_clearance = np.where(act, np.minimum(self.min_clearance, cl), self.min_clearance)
|
| 343 |
+
self.viol_steps += np.where(act & (cl < SAFETY_MARGIN), 1, 0)
|
| 344 |
+
|
| 345 |
+
goal_dist = np.linalg.norm(self.goals - self.pos, axis=2)
|
| 346 |
+
progress = self.prev_goal_dist - goal_dist
|
| 347 |
+
# Proximity shaping. A stronger 2.0/0.8 version was tried and
|
| 348 |
+
# made learning worse (returns ~-12, success ~10%): agents were
|
| 349 |
+
# punished heavily just for starting near hazards. Reverted to
|
| 350 |
+
# the original milder term; the curriculum (trap ramp in
|
| 351 |
+
# train.py) is what teaches avoidance instead.
|
| 352 |
+
reward = 2.0 * progress - 0.02 - 0.5 * np.maximum(0.0, 0.4 - np.where(act, cl, 0.4))
|
| 353 |
+
reward = np.where(act, reward, 0.0)
|
| 354 |
+
|
| 355 |
+
# collisions (checked per kind for reporting)
|
| 356 |
+
d_c = np.linalg.norm(self.pos[:, :, None, :] - self.circles[:, None, :, :2], axis=3) \
|
| 357 |
+
- self.circles[:, None, :, 2]
|
| 358 |
+
hit_circle = (d_c < AGENT_R).any(axis=2)
|
| 359 |
+
seg = self.segments
|
| 360 |
+
px = self.pos[:, :, None, 0]
|
| 361 |
+
py = self.pos[:, :, None, 1]
|
| 362 |
+
cx, cy = _seg_closest(px, py, seg[:, :, 0][:, :, None], seg[:, :, 1][:, :, None], seg[:, :, 2][:, :, None], seg[:, :, 3][:, :, None])
|
| 363 |
+
hit_seg = (np.hypot(px - cx, py - cy)[:, :, 0] < AGENT_R)
|
| 364 |
+
hit_wall = (self.pos[..., 0] < AGENT_R) | (self.pos[..., 0] > ARENA - AGENT_R) | \
|
| 365 |
+
(self.pos[..., 1] < AGENT_R) | (self.pos[..., 1] > ARENA - AGENT_R)
|
| 366 |
+
d_pair = np.linalg.norm(self.pos[:, :, None, :] - self.pos[:, None, :, :], axis=3)
|
| 367 |
+
d_pair[:, np.eye(self.A, dtype=bool)] = np.inf
|
| 368 |
+
d_pair = np.where(self.done_agent[:, None, :], np.inf, d_pair)
|
| 369 |
+
d_pair[:, np.eye(self.A, dtype=bool)] = np.inf
|
| 370 |
+
hit_agent = (d_pair < 2 * AGENT_R).any(axis=2)
|
| 371 |
+
|
| 372 |
+
hit_obstacle = act & (hit_circle | hit_seg | hit_wall)
|
| 373 |
+
hit_other = act & hit_agent
|
| 374 |
+
reached = act & (goal_dist < GOAL_TOL)
|
| 375 |
+
# collision takes precedence over reaching on the same step
|
| 376 |
+
newly_coll = act & (hit_obstacle | hit_other)
|
| 377 |
+
newly_ok = reached & ~newly_coll
|
| 378 |
+
for e in range(self.E):
|
| 379 |
+
for a in range(self.A):
|
| 380 |
+
if not act[e, a]:
|
| 381 |
+
continue
|
| 382 |
+
if hit_obstacle[e, a] and hit_other[e, a]:
|
| 383 |
+
self.collision_kind[e][a] = "obstacle+agent"
|
| 384 |
+
elif hit_obstacle[e, a]:
|
| 385 |
+
self.collision_kind[e][a] = "obstacle"
|
| 386 |
+
elif hit_other[e, a]:
|
| 387 |
+
self.collision_kind[e][a] = "agent"
|
| 388 |
+
self.collision |= newly_coll
|
| 389 |
+
self.success |= newly_ok
|
| 390 |
+
reward = np.where(newly_coll, reward - 10.0, reward)
|
| 391 |
+
reward = np.where(newly_ok, reward + 10.0, reward)
|
| 392 |
+
self.done_agent |= newly_coll | newly_ok
|
| 393 |
+
self.active = ~self.done_agent
|
| 394 |
+
self.prev_goal_dist = goal_dist
|
| 395 |
+
timed_out = (self.steps >= MAX_STEPS)
|
| 396 |
+
ep_done = np.all(self.done_agent, axis=1) | timed_out
|
| 397 |
+
# agents still active at timeout are failures (timeout, not collision)
|
| 398 |
+
if timed_out.any():
|
| 399 |
+
self.active[timed_out] = False
|
| 400 |
+
return self._obs(), reward.astype(np.float32), ep_done, {}
|
| 401 |
+
|
| 402 |
+
|
| 403 |
+
class NavSim:
|
| 404 |
+
"""Single-episode wrapper recording trajectories (for eval/plots)."""
|
| 405 |
+
|
| 406 |
+
def __init__(self, n_agents, seed=0):
|
| 407 |
+
self.core = BatchedSim(1, n_agents, seed=seed)
|
| 408 |
+
self.n_agents = n_agents
|
| 409 |
+
self.traj = None
|
| 410 |
+
self.layout = None
|
| 411 |
+
|
| 412 |
+
def reset(self, layout, obs_noise=0.0, disturbance=0.0):
|
| 413 |
+
self.layout = layout
|
| 414 |
+
self.traj = [ [layout["starts"][i].copy()] for i in range(self.n_agents) ]
|
| 415 |
+
return self.core.reset([layout], obs_noise=obs_noise, disturbance=disturbance)[0]
|
| 416 |
+
|
| 417 |
+
def step(self, action):
|
| 418 |
+
obs, rew, done, _ = self.core.step(action[None, :, :])
|
| 419 |
+
for i in range(self.n_agents):
|
| 420 |
+
self.traj[i].append(self.core.pos[0, i].copy())
|
| 421 |
+
return obs[0], rew[0], bool(done[0]), {}
|
| 422 |
+
|
| 423 |
+
@property
|
| 424 |
+
def pos(self):
|
| 425 |
+
return self.core.pos[0]
|
| 426 |
+
|
| 427 |
+
@property
|
| 428 |
+
def success(self):
|
| 429 |
+
return self.core.success[0]
|
| 430 |
+
|
| 431 |
+
@property
|
| 432 |
+
def collision(self):
|
| 433 |
+
return self.core.collision[0]
|
| 434 |
+
|
| 435 |
+
@property
|
| 436 |
+
def collision_kind(self):
|
| 437 |
+
return self.core.collision_kind[0]
|
| 438 |
+
|
| 439 |
+
@property
|
| 440 |
+
def steps(self):
|
| 441 |
+
return int(self.core.steps[0])
|
train.py
ADDED
|
@@ -0,0 +1,206 @@
|
|
|
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|
|
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|
|
|
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|
|
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|
|
|
|
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|
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|
|
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|
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|
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|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
| 1 |
+
"""PPO training (torch only; BC warm-start via --init, see BUILD_REPORT.md).
|
| 2 |
+
|
| 3 |
+
Training: 16 parallel envs, random layouts regenerated on every episode
|
| 4 |
+
reset (trap probability ramped by --trap-prob/--trap-ramp-iters), fixed seed 0. Rollout =
|
| 5 |
+
128 simulator steps per iteration. Default 120 iterations =
|
| 6 |
+
245,760 environment steps (983,040 agent-steps). PPO: gamma 0.99,
|
| 7 |
+
GAE lambda 0.95, clip 0.2, lr 3e-4, 4 epochs, minibatch 2048,
|
| 8 |
+
entropy coef 0.005 (default; overridable), value coef 0.5, max-grad-norm 0.5.
|
| 9 |
+
Reward is the shaping reward returned by BatchedSim.step
|
| 10 |
+
(progress + success/collision bonuses + step/proximity penalties).
|
| 11 |
+
|
| 12 |
+
Outputs: ppo_policy.pt, training_curve.csv (per-iteration mean return,
|
| 13 |
+
success rate over finished agent-episodes, collision rate, entropy,
|
| 14 |
+
value loss, policy loss), train.log via stdout redirection by caller.
|
| 15 |
+
"""
|
| 16 |
+
import argparse
|
| 17 |
+
import csv
|
| 18 |
+
import json
|
| 19 |
+
import time
|
| 20 |
+
|
| 21 |
+
import numpy as np
|
| 22 |
+
import torch
|
| 23 |
+
import torch.nn as nn
|
| 24 |
+
|
| 25 |
+
from sim import AMAX, OBS_DIM, BatchedSim, generate_layout
|
| 26 |
+
from policies import ActorCritic
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
def main():
|
| 30 |
+
ap = argparse.ArgumentParser()
|
| 31 |
+
ap.add_argument("--iterations", type=int, default=120)
|
| 32 |
+
ap.add_argument("--rollout", type=int, default=128)
|
| 33 |
+
ap.add_argument("--n-envs", type=int, default=16)
|
| 34 |
+
ap.add_argument("--seed", type=int, default=0)
|
| 35 |
+
ap.add_argument("--out", default="ppo_policy.pt")
|
| 36 |
+
ap.add_argument("--curve", default="training_curve.csv")
|
| 37 |
+
ap.add_argument("--n-agents", type=int, default=4)
|
| 38 |
+
ap.add_argument("--init", default="")
|
| 39 |
+
ap.add_argument("--ent-coef", type=float, default=0.005)
|
| 40 |
+
ap.add_argument("--trap-prob", type=float, default=0.5)
|
| 41 |
+
ap.add_argument("--trap-ramp-iters", type=int, default=150)
|
| 42 |
+
args = ap.parse_args()
|
| 43 |
+
torch.manual_seed(args.seed)
|
| 44 |
+
np.random.seed(args.seed)
|
| 45 |
+
torch.set_num_threads(2)
|
| 46 |
+
device = "cpu"
|
| 47 |
+
|
| 48 |
+
net = ActorCritic().to(device)
|
| 49 |
+
if args.init:
|
| 50 |
+
net.load_state_dict(torch.load(args.init, map_location=device, weights_only=True))
|
| 51 |
+
print(f"initialised from {args.init}", flush=True)
|
| 52 |
+
opt = torch.optim.Adam(net.parameters(), lr=3e-4)
|
| 53 |
+
layout_rng = np.random.default_rng(args.seed)
|
| 54 |
+
|
| 55 |
+
def sample_layout():
|
| 56 |
+
# Curriculum: trap probability ramps 0 -> trap_prob over the
|
| 57 |
+
# first trap_ramp_iters iterations, so the policy first learns
|
| 58 |
+
# steering around convex obstacles (short detours) before the
|
| 59 |
+
# wall-trap detour (long, progress-negative detour) appears.
|
| 60 |
+
p = args.trap_prob * min(1.0, (sample_layout.it - 1) / max(args.trap_ramp_iters, 1))
|
| 61 |
+
return generate_layout(layout_rng, args.n_agents, trap=bool(layout_rng.random() < p))
|
| 62 |
+
sample_layout.it = 1
|
| 63 |
+
sim = BatchedSim(args.n_envs, args.n_agents, seed=args.seed)
|
| 64 |
+
layouts = [sample_layout() for _ in range(args.n_envs)]
|
| 65 |
+
obs = sim.reset(layouts)
|
| 66 |
+
|
| 67 |
+
gamma, lam, clip, ent_coef, vf_coef = 0.99, 0.95, 0.2, args.ent_coef, 0.5
|
| 68 |
+
rows = []
|
| 69 |
+
total_env_steps = 0
|
| 70 |
+
t0 = time.time()
|
| 71 |
+
for it in range(1, args.iterations + 1):
|
| 72 |
+
sample_layout.it = it
|
| 73 |
+
obs_buf, act_buf, logp_buf, rew_buf, val_buf, done_buf = [], [], [], [], [], []
|
| 74 |
+
ep_ret, ep_succ, ep_coll, ep_n = [], [], [], 0
|
| 75 |
+
cur_ret = np.zeros((args.n_envs, args.n_agents))
|
| 76 |
+
cur_succ = np.zeros((args.n_envs, args.n_agents), dtype=bool)
|
| 77 |
+
cur_coll = np.zeros((args.n_envs, args.n_agents), dtype=bool)
|
| 78 |
+
prev_done = np.zeros((args.n_envs, args.n_agents), dtype=bool)
|
| 79 |
+
for t in range(args.rollout):
|
| 80 |
+
o = torch.as_tensor(obs)
|
| 81 |
+
with torch.no_grad():
|
| 82 |
+
# pre-tanh Gaussian sample (PPO logprob is on this sample)
|
| 83 |
+
mean, value = net.forward(o)
|
| 84 |
+
std = net.log_std.exp().expand_as(mean)
|
| 85 |
+
dist = torch.distributions.Normal(mean, std)
|
| 86 |
+
raw_s = dist.sample()
|
| 87 |
+
logp = dist.log_prob(raw_s).sum(-1)
|
| 88 |
+
action = torch.tanh(raw_s) * AMAX
|
| 89 |
+
act_np = action.numpy()
|
| 90 |
+
obs_buf.append(obs.copy()); act_buf.append(raw_s.numpy())
|
| 91 |
+
logp_buf.append(logp.numpy()); val_buf.append(value.numpy())
|
| 92 |
+
obs2, rew, ep_done, _ = sim.step(act_np)
|
| 93 |
+
rew_buf.append(rew)
|
| 94 |
+
# per-agent episode end: done_agent flips False->True once
|
| 95 |
+
just_done = sim.done_agent.copy()
|
| 96 |
+
done_buf.append(np.zeros_like(rew, dtype=bool))
|
| 97 |
+
cur_ret += rew
|
| 98 |
+
cur_succ |= sim.success
|
| 99 |
+
cur_coll |= sim.collision
|
| 100 |
+
# episode-level done for GAE: per-agent terminal when its
|
| 101 |
+
# own episode ends (done_agent) or the env timed out
|
| 102 |
+
obs = obs2
|
| 103 |
+
total_env_steps += args.n_envs
|
| 104 |
+
# flush finished agents: done_agent transitions False->True
|
| 105 |
+
newly = just_done & ~prev_done
|
| 106 |
+
if newly.any():
|
| 107 |
+
ep_ret.extend(cur_ret[newly].tolist())
|
| 108 |
+
ep_succ.extend(cur_succ[newly].tolist())
|
| 109 |
+
ep_coll.extend(cur_coll[newly].tolist())
|
| 110 |
+
ep_n += int(newly.sum())
|
| 111 |
+
cur_ret[newly] = 0.0
|
| 112 |
+
cur_succ[newly] = False
|
| 113 |
+
cur_coll[newly] = False
|
| 114 |
+
prev_done = just_done
|
| 115 |
+
# terminal mask for this step: agent done or env done
|
| 116 |
+
done_buf[-1] = just_done | np.broadcast_to(ep_done[:, None], just_done.shape)
|
| 117 |
+
# partial resets for finished envs
|
| 118 |
+
fin = np.where(ep_done)[0]
|
| 119 |
+
if len(fin):
|
| 120 |
+
new_layouts = [sample_layout() for _ in fin]
|
| 121 |
+
obs = sim.reset_envs(fin.tolist(), new_layouts)
|
| 122 |
+
prev_done[fin] = False
|
| 123 |
+
cur_ret[fin] = 0.0
|
| 124 |
+
cur_succ[fin] = False
|
| 125 |
+
cur_coll[fin] = False
|
| 126 |
+
obs_arr = np.stack(obs_buf) # (T,E,A,15)
|
| 127 |
+
act_arr = np.stack(act_buf) # (T,E,A,2) pre-tanh samples
|
| 128 |
+
logp_arr = np.stack(logp_buf) # (T,E,A)
|
| 129 |
+
rew_arr = np.stack(rew_buf).astype(np.float32)
|
| 130 |
+
val_arr = np.stack(val_buf) # (T,E,A)
|
| 131 |
+
done_arr = np.stack(done_buf) # (T,E,A)
|
| 132 |
+
# GAE
|
| 133 |
+
with torch.no_grad():
|
| 134 |
+
_, last_val = net.forward(torch.as_tensor(obs))
|
| 135 |
+
last_val = last_val.numpy()
|
| 136 |
+
adv = np.zeros_like(rew_arr)
|
| 137 |
+
gae = np.zeros(rew_arr.shape[1:])
|
| 138 |
+
for t in reversed(range(args.rollout)):
|
| 139 |
+
nxt_val = last_val if t == args.rollout - 1 else val_arr[t + 1]
|
| 140 |
+
nxt_nonterm = 1.0 - done_arr[t].astype(np.float32)
|
| 141 |
+
delta = rew_arr[t] + gamma * nxt_val * nxt_nonterm - val_arr[t]
|
| 142 |
+
gae = delta + gamma * lam * nxt_nonterm * gae
|
| 143 |
+
adv[t] = gae
|
| 144 |
+
ret_arr = adv + val_arr
|
| 145 |
+
# flatten (T*E*A, ...)
|
| 146 |
+
O = torch.as_tensor(obs_arr.reshape(-1, OBS_DIM))
|
| 147 |
+
A_ = torch.as_tensor(act_arr.reshape(-1, 2))
|
| 148 |
+
LP = torch.as_tensor(logp_arr.reshape(-1))
|
| 149 |
+
ADV = torch.as_tensor(adv.reshape(-1))
|
| 150 |
+
RET = torch.as_tensor(ret_arr.reshape(-1))
|
| 151 |
+
ADV = (ADV - ADV.mean()) / (ADV.std() + 1e-8)
|
| 152 |
+
n = O.shape[0]
|
| 153 |
+
idx = np.arange(n)
|
| 154 |
+
ploss_sum = vloss_sum = ent_sum = 0.0
|
| 155 |
+
nb = 0
|
| 156 |
+
for _epoch in range(4):
|
| 157 |
+
np.random.shuffle(idx)
|
| 158 |
+
for s in range(0, n, 2048):
|
| 159 |
+
b = idx[s:s + 2048]
|
| 160 |
+
logp_new, ent, val_new = net.evaluate(O[b], A_[b])
|
| 161 |
+
ratio = torch.exp(logp_new - LP[b])
|
| 162 |
+
s1 = ratio * ADV[b]
|
| 163 |
+
s2 = torch.clamp(ratio, 1 - clip, 1 + clip) * ADV[b]
|
| 164 |
+
ploss = -torch.min(s1, s2).mean()
|
| 165 |
+
vloss = ((val_new - RET[b]) ** 2).mean()
|
| 166 |
+
loss = ploss + vf_coef * vloss - ent_coef * ent.mean()
|
| 167 |
+
opt.zero_grad()
|
| 168 |
+
loss.backward()
|
| 169 |
+
nn.utils.clip_grad_norm_(net.parameters(), 0.5)
|
| 170 |
+
opt.step()
|
| 171 |
+
ploss_sum += float(ploss.detach()); vloss_sum += float(vloss.detach()); ent_sum += float(ent.detach().mean())
|
| 172 |
+
nb += 1
|
| 173 |
+
row = {
|
| 174 |
+
"iteration": it,
|
| 175 |
+
"env_steps_total": total_env_steps,
|
| 176 |
+
"agent_steps_total": total_env_steps * args.n_agents,
|
| 177 |
+
"mean_episode_return": float(np.mean(ep_ret)) if ep_ret else float("nan"),
|
| 178 |
+
"episodes_finished": ep_n,
|
| 179 |
+
"success_rate_finished": float(np.mean(ep_succ)) if ep_succ else float("nan"),
|
| 180 |
+
"collision_rate_finished": float(np.mean(ep_coll)) if ep_coll else float("nan"),
|
| 181 |
+
"policy_loss": ploss_sum / max(nb, 1),
|
| 182 |
+
"value_loss": vloss_sum / max(nb, 1),
|
| 183 |
+
"entropy": ent_sum / max(nb, 1),
|
| 184 |
+
"elapsed_s": round(time.time() - t0, 1),
|
| 185 |
+
}
|
| 186 |
+
rows.append(row)
|
| 187 |
+
print(f"iter {it}/{args.iterations} env_steps={total_env_steps} "
|
| 188 |
+
f"episodes={ep_n} mean_return={row['mean_episode_return']:.2f} "
|
| 189 |
+
f"success={row['success_rate_finished']:.3f} coll={row['collision_rate_finished']:.3f} "
|
| 190 |
+
f"ploss={row['policy_loss']:.3f} ent={row['entropy']:.3f} "
|
| 191 |
+
f"elapsed={row['elapsed_s']:.0f}s", flush=True)
|
| 192 |
+
if it % 20 == 0 or it == args.iterations:
|
| 193 |
+
torch.save(net.state_dict(), args.out)
|
| 194 |
+
with open(args.curve, "w", newline="") as f:
|
| 195 |
+
w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
|
| 196 |
+
w.writeheader(); w.writerows(rows)
|
| 197 |
+
torch.save(net.state_dict(), args.out)
|
| 198 |
+
with open(args.curve, "w", newline="") as f:
|
| 199 |
+
w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
|
| 200 |
+
w.writeheader(); w.writerows(rows)
|
| 201 |
+
print(f"DONE total_env_steps={total_env_steps} agent_steps={total_env_steps * args.n_agents} "
|
| 202 |
+
f"elapsed={time.time() - t0:.1f}s weights={args.out}")
|
| 203 |
+
|
| 204 |
+
|
| 205 |
+
if __name__ == "__main__":
|
| 206 |
+
main()
|
training_curve.csv
ADDED
|
@@ -0,0 +1,301 @@
|
|
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| 1 |
+
iteration,env_steps_total,agent_steps_total,mean_episode_return,episodes_finished,success_rate_finished,collision_rate_finished,policy_loss,value_loss,entropy,elapsed_s
|
| 2 |
+
1,2048,8192,10.575640301784276,59,0.6271186440677966,0.3728813559322034,-0.004928603550069965,14.59065955877304,2.082795485854149,0.7
|
| 3 |
+
2,4096,16384,2.562062842626195,69,0.5507246376811594,0.4492753623188406,-0.0016877946618478745,12.252745032310486,2.082753822207451,1.4
|
| 4 |
+
3,6144,24576,0.47132985301821817,62,0.3870967741935484,0.6129032258064516,-0.001967126503586769,8.08212098479271,2.080322563648224,2.0
|
| 5 |
+
4,8192,32768,2.4990927434936263,90,0.36666666666666664,0.6333333333333333,-0.002638525838847272,10.998004913330078,2.0817826241254807,2.7
|
| 6 |
+
5,10240,40960,2.3548977821985857,81,0.3950617283950617,0.6049382716049383,-0.003068633552175015,8.251310259103775,2.0817267894744873,3.4
|
| 7 |
+
6,12288,49152,4.02020804057211,81,0.4691358024691358,0.5308641975308642,-0.004024668247438967,8.351954102516174,2.077732175588608,4.1
|
| 8 |
+
7,14336,57344,4.544007849135261,76,0.5394736842105263,0.4605263157894737,-0.002971892070490867,8.952785193920135,2.0765519589185715,4.8
|
| 9 |
+
8,16384,65536,2.1936020220024597,77,0.42857142857142855,0.5714285714285714,-0.0030006902234163135,7.725430727005005,2.077675834298134,5.6
|
| 10 |
+
9,18432,73728,3.4565656299239103,75,0.4666666666666667,0.5333333333333333,-0.002949013367469888,8.198582112789154,2.078346073627472,6.4
|
| 11 |
+
10,20480,81920,1.2308934722174276,88,0.375,0.625,-0.0044605327129829675,8.13099816441536,2.0774028599262238,7.1
|
| 12 |
+
11,22528,90112,2.8204232781586893,91,0.42857142857142855,0.5714285714285714,-0.0030475080420728773,8.678689658641815,2.0769801884889603,7.8
|
| 13 |
+
12,24576,98304,0.8889245347782964,74,0.33783783783783783,0.6621621621621622,-0.002524282957892865,5.834856480360031,2.0732061564922333,8.5
|
| 14 |
+
13,26624,106496,0.04428358325749286,70,0.2857142857142857,0.7142857142857143,-0.002909338567405939,6.503195345401764,2.0692156702280045,9.3
|
| 15 |
+
14,28672,114688,1.5559471067605988,81,0.32098765432098764,0.6790123456790124,-0.003359497830388136,6.851853907108307,2.0641780644655228,10.0
|
| 16 |
+
15,30720,122880,1.6219371619726632,78,0.358974358974359,0.6410256410256411,-0.004129723471123725,6.615370899438858,2.057804897427559,10.7
|
| 17 |
+
16,32768,131072,2.33295302647457,78,0.3717948717948718,0.6282051282051282,-0.0032174318766919896,6.794497847557068,2.0531932562589645,11.5
|
| 18 |
+
17,34816,139264,5.79612788936348,90,0.5222222222222223,0.4777777777777778,-0.0026489434530958533,9.169638335704803,2.0520108491182327,12.2
|
| 19 |
+
18,36864,147456,4.276922380308538,72,0.5138888888888888,0.4861111111111111,-0.0027611292025540024,6.842963308095932,2.050378054380417,12.9
|
| 20 |
+
19,38912,155648,4.9903606851391045,86,0.5465116279069767,0.45348837209302323,-0.003078278386965394,8.408494651317596,2.042968362569809,13.6
|
| 21 |
+
20,40960,163840,2.266854671356896,80,0.3625,0.6375,-0.0015407394093926996,8.068686366081238,2.037264660000801,14.4
|
| 22 |
+
21,43008,172032,2.9792227984840833,86,0.3953488372093023,0.6046511627906976,-0.0034769773337757215,9.113058745861053,2.034867584705353,15.1
|
| 23 |
+
22,45056,180224,0.030156758302260507,82,0.2926829268292683,0.7073170731707317,-0.0027939462306676432,7.07372909784317,2.037084072828293,15.9
|
| 24 |
+
23,47104,188416,3.2074196618111777,90,0.36666666666666664,0.6333333333333333,-0.0031349235359812155,7.413722187280655,2.041795939207077,16.6
|
| 25 |
+
24,49152,196608,3.4296138582416664,71,0.4225352112676056,0.5774647887323944,-0.0033239419863093644,6.451312720775604,2.043825536966324,17.2
|
| 26 |
+
25,51200,204800,3.2999859574226322,80,0.4625,0.5375,-0.0025079312326852232,6.926906108856201,2.0443067401647568,18.0
|
| 27 |
+
26,53248,212992,3.8189263474791613,60,0.4666666666666667,0.5333333333333333,-0.002866165596060455,6.08053594827652,2.043127402663231,18.7
|
| 28 |
+
27,55296,221184,3.021201148037489,93,0.45161290322580644,0.5483870967741935,-0.001985581679036841,7.476691424846649,2.0378994941711426,19.5
|
| 29 |
+
28,57344,229376,2.4402852603493903,79,0.4050632911392405,0.5949367088607594,-0.0023241962044266984,5.752610236406326,2.034714698791504,20.2
|
| 30 |
+
29,59392,237568,5.966961281539326,79,0.46835443037974683,0.5316455696202531,-0.0012439779711712617,7.7765122056007385,2.0389565378427505,21.1
|
| 31 |
+
30,61440,245760,3.7504845612227453,72,0.5,0.5,-0.0027182766352780163,6.2421844601631165,2.0409526377916336,21.8
|
| 32 |
+
31,63488,253952,1.6922513383864257,83,0.37349397590361444,0.6265060240963856,-0.0027743616665247828,7.865664720535278,2.041315972805023,22.6
|
| 33 |
+
32,65536,262144,2.42512364087861,96,0.3541666666666667,0.6458333333333334,-0.0038563719717785716,7.003673791885376,2.0413284450769424,23.3
|
| 34 |
+
33,67584,270336,2.1625593843448434,76,0.35526315789473684,0.6447368421052632,-0.0032946230785455555,6.754074156284332,2.041317567229271,23.9
|
| 35 |
+
34,69632,278528,3.254253022030163,74,0.40540540540540543,0.5945945945945946,-0.0022394688785425387,6.230794191360474,2.0404125452041626,24.6
|
| 36 |
+
35,71680,286720,2.80636341853298,83,0.42168674698795183,0.5783132530120482,-0.0027435466545284726,7.972445785999298,2.0382582247257233,25.2
|
| 37 |
+
36,73728,294912,3.6626795505080514,82,0.43902439024390244,0.5609756097560976,-0.0030186151998350397,7.576087325811386,2.0357282906770706,26.0
|
| 38 |
+
37,75776,303104,4.482805713320067,80,0.45,0.55,-0.0029039245418971404,7.200312286615372,2.0348491221666336,26.7
|
| 39 |
+
38,77824,311296,1.3526471700564084,70,0.38571428571428573,0.6142857142857143,-0.0014371996803674847,7.642363995313644,2.035452902317047,27.3
|
| 40 |
+
39,79872,319488,3.685284045022065,83,0.37349397590361444,0.6265060240963856,-0.0022183082764968276,7.732052445411682,2.0340425074100494,28.1
|
| 41 |
+
40,81920,327680,3.2041106211404586,62,0.3387096774193548,0.6612903225806451,-0.003152919511194341,5.132548660039902,2.0332902520895004,28.9
|
| 42 |
+
41,83968,335872,2.5305882948272944,91,0.4065934065934066,0.5934065934065934,-0.0022030029795132577,7.531362891197205,2.031634107232094,29.7
|
| 43 |
+
42,86016,344064,3.88695862614541,67,0.3880597014925373,0.6119402985074627,-0.002808443910907954,5.475524514913559,2.029216393828392,30.3
|
| 44 |
+
43,88064,352256,4.529799214478624,74,0.527027027027027,0.47297297297297297,-0.0028709975304082036,7.077459633350372,2.0272628366947174,31.3
|
| 45 |
+
44,90112,360448,4.963569099306883,69,0.463768115942029,0.5362318840579711,-0.0030373218469321728,7.0344259440898895,2.0274009108543396,32.0
|
| 46 |
+
45,92160,368640,4.90286181322881,72,0.5416666666666666,0.4583333333333333,-0.0019289599877083674,6.2651347219944,2.0290433317422867,32.6
|
| 47 |
+
46,94208,376832,2.893481967114591,75,0.41333333333333333,0.5866666666666667,-0.0025801387091632932,6.944160163402557,2.032102957367897,33.2
|
| 48 |
+
47,96256,385024,3.5697200894798544,82,0.45121951219512196,0.5487804878048781,-0.002958324665087275,6.755562216043472,2.0331098437309265,34.0
|
| 49 |
+
48,98304,393216,4.550713637062,75,0.44,0.56,-0.001404331618687138,7.348447620868683,2.0341212451457977,34.8
|
| 50 |
+
49,100352,401408,1.033377697824617,75,0.3333333333333333,0.6666666666666666,-0.0022904583311174065,6.085790604352951,2.0340641140937805,35.5
|
| 51 |
+
50,102400,409600,2.4666385712852286,72,0.3472222222222222,0.6527777777777778,-0.0024065238249022514,5.462814182043076,2.033889502286911,36.3
|
| 52 |
+
51,104448,417792,4.506897245002663,83,0.42168674698795183,0.5783132530120482,-0.0019391112145967782,7.028240978717804,2.034760981798172,36.9
|
| 53 |
+
52,106496,425984,7.533366283298254,61,0.6557377049180327,0.3442622950819672,-0.0026932698383461684,6.476862788200378,2.0341406017541885,37.6
|
| 54 |
+
53,108544,434176,1.708138642005994,62,0.532258064516129,0.46774193548387094,-0.0033126597409136593,5.225630134344101,2.032433971762657,38.3
|
| 55 |
+
54,110592,442368,4.311251982919086,82,0.4634146341463415,0.5365853658536586,-0.0030828304297756404,7.31951242685318,2.0319934338331223,38.9
|
| 56 |
+
55,112640,450560,2.171235135670791,73,0.3698630136986301,0.6301369863013698,-0.0028378469578456134,6.226896643638611,2.029710531234741,39.6
|
| 57 |
+
56,114688,458752,4.560326129896202,80,0.475,0.525,-0.0029576695487776306,7.076795190572739,2.027935117483139,40.3
|
| 58 |
+
57,116736,466944,5.987806920215872,61,0.5245901639344263,0.47540983606557374,-0.0024381950788665563,6.181976556777954,2.0304195880889893,41.0
|
| 59 |
+
58,118784,475136,2.1036425462696773,90,0.4777777777777778,0.5222222222222223,-0.0035929154546465725,7.433887839317322,2.0327984541654587,41.6
|
| 60 |
+
59,120832,483328,5.15335603554981,62,0.4838709677419355,0.5161290322580645,-0.001889711566036567,5.0034909546375275,2.030978187918663,42.2
|
| 61 |
+
60,122880,491520,3.4565554077389473,69,0.4782608695652174,0.5217391304347826,-0.0017213669489137828,7.342305451631546,2.031145825982094,42.9
|
| 62 |
+
61,124928,499712,4.207035402465194,68,0.4411764705882353,0.5588235294117647,-0.002210144608397968,5.655366510152817,2.0315774232149124,43.6
|
| 63 |
+
62,126976,507904,3.8952850935956524,72,0.4305555555555556,0.5694444444444444,-0.0028251620824448764,7.787546902894974,2.03234800696373,44.3
|
| 64 |
+
63,129024,516096,3.809050839074548,81,0.43209876543209874,0.5679012345679012,-0.0028237917576916516,7.464680969715118,2.0321473479270935,45.0
|
| 65 |
+
64,131072,524288,4.470689786581041,81,0.5185185185185185,0.48148148148148145,-0.0032206666364800185,7.291623890399933,2.033550903201103,45.8
|
| 66 |
+
65,133120,532480,2.823766911122992,66,0.45454545454545453,0.5454545454545454,-0.0022931132480152883,6.518039882183075,2.0356991589069366,46.5
|
| 67 |
+
66,135168,540672,7.219030763190975,62,0.5967741935483871,0.4032258064516129,-0.002140324722859077,5.504665404558182,2.0367358326911926,47.2
|
| 68 |
+
67,137216,548864,5.109908520462853,71,0.6056338028169014,0.39436619718309857,-0.0028077360475435853,7.150039792060852,2.036020800471306,47.8
|
| 69 |
+
68,139264,557056,4.470355470626882,71,0.5352112676056338,0.4647887323943662,-0.0029366682283580303,6.816843241453171,2.0336417853832245,48.6
|
| 70 |
+
69,141312,565248,4.006923821809376,69,0.4927536231884058,0.5072463768115942,-0.0032388644467573613,6.9354806542396545,2.0369324535131454,49.4
|
| 71 |
+
70,143360,573440,5.48630763256531,81,0.5185185185185185,0.48148148148148145,-0.003506150722387247,6.922869235277176,2.0369001775979996,50.0
|
| 72 |
+
71,145408,581632,6.276496978711759,86,0.5348837209302325,0.46511627906976744,-0.0024871523200999945,7.800946235656738,2.033847078680992,50.7
|
| 73 |
+
72,147456,589824,2.0517811295790476,78,0.44871794871794873,0.5512820512820513,-0.001771871087839827,8.007484346628189,2.0316931009292603,51.4
|
| 74 |
+
73,149504,598016,3.508949891496372,79,0.4050632911392405,0.5949367088607594,-0.0028410120285116136,6.439613550901413,2.031848281621933,52.2
|
| 75 |
+
74,151552,606208,3.955355725445674,76,0.5131578947368421,0.4868421052631579,-0.0024140097957570106,6.144068092107773,2.0327788293361664,52.9
|
| 76 |
+
75,153600,614400,2.9825256775213225,59,0.4745762711864407,0.5254237288135594,-0.002452779284794815,6.378163456916809,2.032555565237999,53.6
|
| 77 |
+
76,155648,622592,5.400847100474584,80,0.575,0.425,-0.002404216182185337,7.749052107334137,2.029635801911354,54.3
|
| 78 |
+
77,157696,630784,4.810888903765426,71,0.5352112676056338,0.4647887323943662,-0.002284207264892757,6.48259174823761,2.026926338672638,55.0
|
| 79 |
+
78,159744,638976,5.10449211155906,74,0.5540540540540541,0.44594594594594594,-0.0031203972321236506,8.050639152526855,2.024889960885048,55.6
|
| 80 |
+
79,161792,647168,3.6473804921025925,67,0.5223880597014925,0.47761194029850745,-0.0026033056783489883,6.79375022649765,2.0242442339658737,56.4
|
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| 275 |
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| 300 |
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299,612352,2449408,4.899870044295312,64,0.59375,0.40625,-0.0023492122563766316,6.032012015581131,1.8871031999588013,216.1
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| 301 |
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|
training_curve_ft1.csv
ADDED
|
@@ -0,0 +1,301 @@
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|
| 1 |
+
iteration,env_steps_total,agent_steps_total,mean_episode_return,episodes_finished,success_rate_finished,collision_rate_finished,policy_loss,value_loss,entropy,elapsed_s
|
| 2 |
+
1,2048,2048,11.839879118695839,15,0.6666666666666666,0.3333333333333333,-0.0011567804031074047,10.67442512512207,0.8370100110769272,0.6
|
| 3 |
+
2,4096,4096,13.12561026549693,21,0.8571428571428571,0.14285714285714285,-0.005822745151817799,12.819827556610107,0.8353047072887421,1.0
|
| 4 |
+
3,6144,6144,11.434010576311266,28,0.75,0.25,-0.0020985418814234436,16.787265300750732,0.8347393274307251,1.3
|
| 5 |
+
4,8192,8192,12.58344480084074,26,0.8076923076923077,0.19230769230769232,-0.00170546694425866,15.314281940460205,0.8344054967164993,1.6
|
| 6 |
+
5,10240,10240,11.019174287935021,25,0.76,0.24,-0.005013727772166021,13.093919277191162,0.8345679938793182,1.9
|
| 7 |
+
6,12288,12288,9.382823554993365,28,0.6428571428571429,0.35714285714285715,-0.0011232034303247929,15.145228385925293,0.8346153199672699,2.4
|
| 8 |
+
7,14336,14336,10.063474413637495,29,0.7241379310344828,0.27586206896551724,-0.0006399849662557244,14.941529273986816,0.8341886699199677,3.2
|
| 9 |
+
8,16384,16384,10.699887853811333,32,0.75,0.25,-0.004131294437684119,15.525076627731323,0.8334957361221313,3.5
|
| 10 |
+
9,18432,18432,12.014305467169228,27,0.7407407407407407,0.25925925925925924,-0.0028663385310210288,13.910238265991211,0.8327423334121704,3.9
|
| 11 |
+
10,20480,20480,11.784133904239,23,0.782608695652174,0.21739130434782608,-0.002799128240440041,11.448911666870117,0.8322525471448898,4.3
|
| 12 |
+
11,22528,22528,6.5477757013013616,32,0.5625,0.4375,-0.0022213649353943765,15.645389318466187,0.8316848427057266,4.6
|
| 13 |
+
12,24576,24576,11.180556660875826,29,0.7586206896551724,0.2413793103448276,-0.002867242321372032,13.472214937210083,0.8310301154851913,4.9
|
| 14 |
+
13,26624,26624,8.690535040796021,32,0.65625,0.34375,-0.0011255578720010817,14.347780704498291,0.8299856930971146,5.3
|
| 15 |
+
14,28672,28672,10.525023922108042,34,0.7058823529411765,0.29411764705882354,-0.002648460096679628,15.281294107437134,0.829058438539505,5.7
|
| 16 |
+
15,30720,30720,10.353933712008939,32,0.6875,0.3125,-0.0007922212826088071,14.889341354370117,0.8281810581684113,6.1
|
| 17 |
+
16,32768,32768,9.902292013721246,29,0.6896551724137931,0.3103448275862069,-0.0020128731848672032,12.77059006690979,0.8278558999300003,6.5
|
| 18 |
+
17,34816,34816,9.778110388575065,29,0.6551724137931034,0.3448275862068966,-0.0020610735518857837,12.817755460739136,0.8279525339603424,7.1
|
| 19 |
+
18,36864,36864,10.166859841376032,31,0.7096774193548387,0.2903225806451613,-0.0027632241253741086,13.1688973903656,0.8281720578670502,7.4
|
| 20 |
+
19,38912,38912,8.387802106848111,17,0.5882352941176471,0.4117647058823529,-0.003304500598460436,8.273476362228394,0.8287307024002075,7.7
|
| 21 |
+
20,40960,40960,10.974399606018258,28,0.7142857142857143,0.2857142857142857,0.006031866185367107,12.519166946411133,0.8298032730817795,8.1
|
| 22 |
+
21,43008,43008,4.978011854742363,24,0.5,0.5,-0.001903820433653891,12.335115432739258,0.831254169344902,8.4
|
| 23 |
+
22,45056,45056,9.905587265687064,20,0.65,0.35,-0.0008802864467725158,9.613081216812134,0.8335110247135162,8.7
|
| 24 |
+
23,47104,47104,12.7682526100607,16,0.8125,0.1875,-0.0006630003626924008,6.997617125511169,0.8349899351596832,9.1
|
| 25 |
+
24,49152,49152,11.440951142116319,20,0.75,0.25,0.0011139045818708837,7.72308886051178,0.836490735411644,9.4
|
| 26 |
+
25,51200,51200,9.281041903818613,23,0.6956521739130435,0.30434782608695654,-0.003134614322334528,9.90314245223999,0.8383462280035019,9.8
|
| 27 |
+
26,53248,53248,12.18827449567228,24,0.75,0.25,0.0009708477300591767,10.061233043670654,0.8409299254417419,10.1
|
| 28 |
+
27,55296,55296,11.302204908245722,20,0.75,0.25,-0.0007570137386210263,7.66024911403656,0.8431023508310318,10.5
|
| 29 |
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28,57344,57344,6.252375650532832,21,0.5238095238095238,0.47619047619047616,-0.0032617637771181762,13.654354572296143,0.8445560932159424,10.9
|
| 30 |
+
29,59392,59392,9.08166033209144,17,0.6470588235294118,0.35294117647058826,-0.001073246414307505,8.523426055908203,0.8454207330942154,11.2
|
| 31 |
+
30,61440,61440,7.111795057296986,25,0.6,0.4,-0.0014987583854235709,12.626418352127075,0.8457401096820831,11.6
|
| 32 |
+
31,63488,63488,13.470849790668746,22,0.8181818181818182,0.18181818181818182,-0.000987873412668705,8.281080722808838,0.8449421375989914,11.9
|
| 33 |
+
32,65536,65536,8.634966244409442,25,0.64,0.36,-0.002631734620081261,11.861751556396484,0.8441851884126663,12.3
|
| 34 |
+
33,67584,67584,7.87456971559597,27,0.6296296296296297,0.37037037037037035,-0.0008545421878807247,13.090773582458496,0.8438760489225388,12.7
|
| 35 |
+
34,69632,69632,8.520761461026268,20,0.65,0.35,-0.001582831348059699,10.492756843566895,0.8431892395019531,13.0
|
| 36 |
+
35,71680,71680,9.942144434879944,19,0.7368421052631579,0.2631578947368421,-0.001267218787688762,7.527131199836731,0.8417492359876633,13.4
|
| 37 |
+
36,73728,73728,9.031363889971736,19,0.6842105263157895,0.3157894736842105,-0.0020451597520150244,8.33088755607605,0.8400571048259735,13.7
|
| 38 |
+
37,75776,75776,7.35206208769731,14,0.5714285714285714,0.42857142857142855,-0.0019027317175641656,9.604303359985352,0.8379762917757034,14.1
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| 39 |
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|
training_curve_ft2.csv
ADDED
|
@@ -0,0 +1,301 @@
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|
| 1 |
+
iteration,env_steps_total,agent_steps_total,mean_episode_return,episodes_finished,success_rate_finished,collision_rate_finished,policy_loss,value_loss,entropy,elapsed_s
|
| 2 |
+
1,2048,2048,12.235706851434381,14,0.6428571428571429,0.35714285714285715,-0.0038270954973995686,6.414763569831848,1.838270753622055,0.8
|
| 3 |
+
2,4096,4096,2.1782318589121132,16,0.4375,0.5625,-0.0015612796414643526,12.003445148468018,1.8401535749435425,1.2
|
| 4 |
+
3,6144,6144,4.584766940674924,13,0.5384615384615384,0.46153846153846156,-0.001720457396004349,3.7244012355804443,1.8413429856300354,1.7
|
| 5 |
+
4,8192,8192,6.2549766906304285,5,0.6,0.4,-0.0023948889574967325,1.839277595281601,1.841403990983963,2.0
|
| 6 |
+
5,10240,10240,19.02289472980192,2,1.0,0.0,-0.001265872357180342,1.6035058200359344,1.8410905599594116,2.4
|
| 7 |
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299,612352,612352,13.283057092909901,22,0.7727272727272727,0.22727272727272727,-0.001483697909861803,8.739694595336914,2.057634949684143,111.9
|
| 301 |
+
300,614400,614400,10.684474068319625,21,0.7142857142857143,0.2857142857142857,-0.0023641863954253495,8.145326375961304,2.05841600894928,112.3
|
training_curve_ft3.csv
ADDED
|
@@ -0,0 +1,301 @@
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|
| 1 |
+
iteration,env_steps_total,agent_steps_total,mean_episode_return,episodes_finished,success_rate_finished,collision_rate_finished,policy_loss,value_loss,entropy,elapsed_s
|
| 2 |
+
1,2048,2048,-1.3388641727755644,14,0.21428571428571427,0.7857142857142857,-0.004610374919138849,15.503142833709717,2.0595927238464355,1.7
|
| 3 |
+
2,4096,4096,1.0116734897289592,7,0.2857142857142857,0.7142857142857143,-0.003178040497004986,14.405221223831177,2.0617396235466003,2.1
|
| 4 |
+
3,6144,6144,4.550857264691413,22,0.4090909090909091,0.5909090909090909,-0.004194581764750183,19.263060569763184,2.0633803606033325,2.4
|
| 5 |
+
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|
| 6 |
+
5,10240,10240,11.446790603005166,13,0.6923076923076923,0.3076923076923077,-0.002349212532863021,8.179303407669067,2.0664291977882385,3.2
|
| 7 |
+
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|
| 8 |
+
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|
| 9 |
+
8,16384,16384,10.114363732514448,16,0.75,0.25,-0.002815337269566953,5.556365013122559,2.067994236946106,4.7
|
| 10 |
+
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|
| 11 |
+
10,20480,20480,7.737498361237158,16,0.625,0.375,-0.00024301500525325537,11.178643465042114,2.067386507987976,5.4
|
| 12 |
+
11,22528,22528,8.67612427729182,17,0.6470588235294118,0.35294117647058826,-0.0022554018651135266,9.34282636642456,2.0671146512031555,5.7
|
| 13 |
+
12,24576,24576,8.89060429155851,19,0.631578947368421,0.3684210526315789,-0.0018543325131759048,9.896521806716919,2.066668927669525,6.1
|
| 14 |
+
13,26624,26624,18.958391986228527,10,1.0,0.0,-0.0012815098743885756,3.476322889328003,2.0655145049095154,6.4
|
| 15 |
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14,28672,28672,8.57671289875569,14,0.7142857142857143,0.2857142857142857,-0.00024456268874928355,9.725223302841187,2.0645389556884766,7.1
|
| 16 |
+
15,30720,30720,11.032428103989028,12,0.75,0.25,-0.002428624138701707,6.7704795598983765,2.064236581325531,7.4
|
| 17 |
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16,32768,32768,10.08138089584544,18,0.6666666666666666,0.3333333333333333,-0.001644148665945977,8.175562620162964,2.063873767852783,7.8
|
| 18 |
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17,34816,34816,18.088928498201614,18,1.0,0.0,-0.0009306026622653008,4.599920153617859,2.0635181069374084,8.2
|
| 19 |
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|
| 20 |
+
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|
| 21 |
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|
| 22 |
+
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|
| 23 |
+
22,45056,45056,15.445966754750406,15,0.8666666666666667,0.13333333333333333,-0.001161016640253365,5.145367980003357,2.058764398097992,10.2
|
| 24 |
+
23,47104,47104,12.866243162701721,16,0.8125,0.1875,-0.0015610510017722845,5.6321516036987305,2.0586562752723694,10.5
|
| 25 |
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|
| 26 |
+
25,51200,51200,4.380317129440664,20,0.45,0.55,-0.0012186687599751167,14.456429243087769,2.059312582015991,11.2
|
| 27 |
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26,53248,53248,11.629472222969136,21,0.7619047619047619,0.23809523809523808,-0.00033692410215735435,6.766394138336182,2.059269607067108,11.6
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| 28 |
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27,55296,55296,12.879245117063874,20,0.8,0.2,-0.002716035465709865,6.653873085975647,2.0588103532791138,11.9
|
| 29 |
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28,57344,57344,12.591423161722647,21,0.8095238095238095,0.19047619047619047,-0.0011507038725540042,7.199720025062561,2.058172643184662,12.3
|
| 30 |
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|
| 31 |
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30,61440,61440,11.901623923693,19,0.7894736842105263,0.21052631578947367,-0.0014067725860513747,7.1038113832473755,2.0576183795928955,13.0
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| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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34,69632,69632,12.740133586112538,14,0.7857142857142857,0.21428571428571427,-0.0023503192351199687,4.864396572113037,2.056591749191284,14.9
|
| 36 |
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35,71680,71680,12.869411134069436,19,0.8421052631578947,0.15789473684210525,-0.0030885701125953346,4.9380844831466675,2.0569175481796265,15.5
|
| 37 |
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| 38 |
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37,75776,75776,15.040889353372222,8,0.875,0.125,-0.0021499795839190483,4.0283021330833435,2.056238532066345,16.6
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| 39 |
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| 40 |
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| 41 |
+
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|
| 42 |
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|
| 43 |
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42,86016,86016,18.344251360930837,12,1.0,0.0,-0.0012631186400540173,2.9404008388519287,2.057289719581604,18.5
|
| 44 |
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|
| 45 |
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44,90112,90112,13.398837968536291,19,0.7894736842105263,0.21052631578947367,-0.0030169536476023495,5.438039660453796,2.058473527431488,19.2
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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51,104448,104448,15.924567566232508,20,0.9,0.1,0.00035019987262785435,4.445795059204102,2.0523690581321716,21.7
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| 53 |
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| 54 |
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| 55 |
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|
| 56 |
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| 57 |
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|
| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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|
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