| # SLM Lab Improvements Roadmap | |
| SLM Lab's algorithms (PPO, SAC) are architecturally sound but use 2017-era defaults. This roadmap integrates material advances from the post-PPO RL landscape. | |
| **Source**: [`notes/literature/ai/rl-landscape-2026.md`](../../notes/literature/ai/rl-landscape-2026.md) | |
| **Hardware**: Mac (Apple Silicon) for dev, cloud GPU (A100/H100) for runs. | |
| --- | |
| ## Status | |
| | Step | What | Status | | |
| |:---:|------|--------| | |
| | **1** | **GPU envs (MuJoCo Playground)** | **NEXT** | | |
| | 2 | Normalization stack (layer norm, percentile) | DONE | | |
| | 3 | CrossQ algorithm (batch norm critics) | DONE | | |
| | 4 | Combine + full benchmark suite | TODO (after Step 1) | | |
| | 5 | High-UTD SAC / RLPD | TODO | | |
| | 6 | Pretrained vision encoders | TODO | | |
| --- | |
| ## NEXT: Step 1 — GPU Envs (MuJoCo Playground) | |
| **Goal**: Remove env as the bottleneck. Run physics on GPU via [MuJoCo Playground](https://github.com/google-deepmind/mujoco_playground), keep training in PyTorch. Scale to 1000+ parallel envs for large-scale runs. | |
| ### The Stack | |
| ``` | |
| MuJoCo Playground ← env definitions, registry, wrappers | |
| ↓ | |
| Brax ← EpisodeWrapper, AutoResetWrapper | |
| ↓ | |
| MuJoCo MJX ← JAX reimplementation of MuJoCo physics (GPU/TPU) | |
| ↓ | |
| JAX / XLA ← jit, vmap | |
| ``` | |
| ### API Difference | |
| Playground uses a **stateless functional API**, not Gymnasium OOP: | |
| ```python | |
| # Gymnasium (today) # Playground | |
| env = gym.make("HalfCheetah-v5") env = registry.load("CheetahRun") | |
| obs, info = env.reset() state = env.reset(rng) # → State dataclass | |
| obs, rew, term, trunc, info = env.step(a) state = env.step(state, a) # → new State | |
| ``` | |
| Key differences: functional (state passed explicitly), `jax.vmap` for batching (not `VectorEnv`), `jax.jit` for GPU compilation, single `done` flag (no term/trunc split), `observation_size`/`action_size` ints (no `gym.spaces`). | |
| ### Environment Catalog | |
| **DM Control Suite (25 envs)** — standard RL benchmarks, but dm_control versions (different obs/reward/termination from Gymnasium MuJoCo): | |
| | Playground | Nearest Gymnasium | Notes | | |
| |-----------|-------------------|-------| | |
| | `CheetahRun` | `HalfCheetah-v5` | Tolerance reward (target speed=10) | | |
| | `HopperHop` / `HopperStand` | `Hopper-v5` | Different reward | | |
| | `WalkerWalk` / `WalkerRun` | `Walker2d-v5` | dm_control version | | |
| | `HumanoidWalk` / `HumanoidRun` | `Humanoid-v5` | CMU humanoid | | |
| | `CartpoleSwingup` | `CartPole-v1` | Swing-up (harder) | | |
| | `ReacherEasy/Hard`, `FingerSpin/Turn*`, `FishSwim`, `PendulumSwingup`, `SwimmerSwimmer6` | — | Various | | |
| No Ant equivalent. Results NOT comparable across env suites. | |
| **Locomotion (19 envs)** — real robots (Unitree Go1/G1/H1, Spot, etc.) with joystick control, gait tracking, recovery. | |
| **Manipulation (10 envs)** — Aloha bimanual, Franka Panda, LEAP hand dexterity. | |
| ### Performance | |
| Single-env MJX is ~10x slower than CPU MuJoCo. The win comes from massive parallelism: | |
| | Hardware | Batch Size | Humanoid steps/sec | | |
| |----------|-----------|-------------------| | |
| | M3 Max (CPU) | ~128 | 650K | | |
| | A100 (MJX) | 8,192 | 950K | | |
| Training throughput on single A100: ~720K steps/sec (Cartpole PPO), ~91K steps/sec (Humanoid PPO). SAC 25-50x slower than PPO (off-policy overhead). | |
| **Wall clock (1M frames)**: CPU ~80 min → GPU <5 min (PPO), ~30 min (SAC). | |
| ### Integration Design | |
| Adapter at the env boundary. Algorithms unchanged. | |
| ``` | |
| Spec: env.backend = "playground", env.name = "CheetahRun", env.num_envs = 4096 | |
| ↓ | |
| make_env() routes on backend | |
| ↓ | |
| PlaygroundVecEnv(VectorEnv) ← jit+vmap internally, DLPack zero-copy at boundary | |
| ↓ | |
| VectorClockWrapper → Session.run_rl() (existing, unchanged) | |
| ``` | |
| Reference implementations: Playground's [`wrapper_torch.py`](https://github.com/google-deepmind/mujoco_playground/blob/main/mujoco_playground/_src/wrapper_torch.py) (`RSLRLBraxWrapper`), [skrl](https://skrl.readthedocs.io/en/develop/api/envs/wrapping.html) Gymnasium-like wrapper. | |
| ### Changes | |
| - `slm_lab/env/playground.py`: **New** — `PlaygroundVecEnv(VectorEnv)` adapter (JIT, vmap, DLPack, auto-reset, RNG management) | |
| - `slm_lab/env/__init__.py`: `backend` routing in `make_env()` | |
| - `pyproject.toml`: Optional `[playground]` dependency group (`mujoco-playground`, `jax[cuda12]`, `mujoco-mjx`, `brax`) | |
| - Specs: New specs with `backend: playground`, Playground env names, `num_envs: 4096` | |
| No changes to: algorithms, networks, memory, training loop, experiment control. | |
| ### Gotchas | |
| 1. **JIT startup**: First `reset()`/`step()` triggers XLA compilation (10-60s). One-time. | |
| 2. **Static shapes**: `num_envs` fixed at construction. Contacts padded to max possible. | |
| 3. **Ampere precision**: RTX 30/40 need `JAX_DEFAULT_MATMUL_PRECISION=highest` or training destabilizes. | |
| 4. **No Atari**: Playground is physics-only. Atari stays on CPU Gymnasium. | |
| ### Verify | |
| PPO on CheetahRun — same reward as CPU baseline, 100x+ faster wall clock (4096 envs, A100). | |
| ### Migration Path | |
| 1. **Phase 1** (this step): Adapter + DM Control locomotion (CheetahRun, HopperHop, WalkerWalk, HumanoidWalk/Run) | |
| 2. **Phase 2**: Robotics envs (Unitree Go1/G1, Spot, Franka Panda, LEAP hand) | |
| 3. **Phase 3**: Isaac Lab (same adapter pattern, PhysX backend, sim-to-real) | |
| --- | |
| ## TODO: Step 4 — Combine + Full Benchmark Suite | |
| **Goal**: Run PPO v2 and CrossQ+norm on MuJoCo envs. Record wall-clock and final reward (mean ± std, 4 seeds). This is the "before/after" comparison for the roadmap. | |
| **Runs to dispatch** (via dstack, see `docs/BENCHMARKS.md`): | |
| | Algorithm | Env | Spec | Frames | | |
| |-----------|-----|------|--------| | |
| | PPO v2 | HalfCheetah-v5 | `ppo_mujoco_v2_arc.yaml` | 1M | | |
| | PPO v2 | Humanoid-v5 | `ppo_mujoco_v2_arc.yaml` | 2M | | |
| | SAC v2 | HalfCheetah-v5 | `sac_mujoco_v2_arc.yaml` | 1M | | |
| | SAC v2 | Humanoid-v5 | `sac_mujoco_v2_arc.yaml` | 2M | | |
| | CrossQ | HalfCheetah-v5 | `crossq_mujoco.yaml` | 4M | | |
| | CrossQ | Humanoid-v5 | `crossq_mujoco.yaml` | 1M | | |
| | CrossQ | Hopper-v5 | `crossq_mujoco.yaml` | 3M | | |
| | CrossQ | Ant-v5 | `crossq_mujoco.yaml` | 2M | | |
| **Verify**: Both algorithms beat their v1/SAC baselines on at least 2/3 envs. | |
| **Local testing results (200k frames, 4 sessions)**: | |
| - PPO v2 (layer norm + percentile) beats baseline on Humanoid (272.67 vs 246.83, consistency 0.78 vs 0.70) | |
| - Layer norm is the most reliable individual feature — helps on LunarLander (+56%) and Humanoid (+8%) | |
| - CrossQ beats SAC on CartPole (383 vs 238), Humanoid (365 vs 356), with higher consistency | |
| - CrossQ unstable on Ant (loss divergence) — may need tuning for high-dimensional action spaces | |
| --- | |
| ## Completed: Step 2 — Normalization Stack | |
| **v2 = layer_norm + percentile normalization** (symlog dropped — harms model-free RL). | |
| Changes: | |
| - `net_util.py` / `mlp.py`: `layer_norm` and `batch_norm` params in `build_fc_model()` / `MLPNet` | |
| - `actor_critic.py`: `PercentileNormalizer` (EMA-tracked 5th/95th percentile advantage normalization) | |
| - `math_util.py`: `symlog` / `symexp` (retained but excluded from v2 defaults) | |
| - `ppo.py` / `sac.py`: symlog + percentile normalization integration | |
| - Specs: `ppo_mujoco_v2_arc.yaml`, `sac_mujoco_v2_arc.yaml` | |
| ## Completed: Step 3 — CrossQ | |
| CrossQ: SAC variant with no target networks. Uses cross-batch normalization on concatenated (s,a) and (s',a') batches. | |
| Changes: | |
| - `crossq.py`: CrossQ algorithm inheriting from SAC | |
| - `algorithm/__init__.py`: CrossQ import | |
| - Specs: `benchmark/crossq/crossq_mujoco.yaml`, `crossq_classic.yaml`, `crossq_box2d.yaml`, `crossq_atari.yaml` | |
| - Future: actor LayerNorm (TorchArc YAML) — may help underperforming envs (Hopper, InvPendulum, Humanoid) | |
| --- | |
| ## TODO: Step 5 — High-UTD SAC / RLPD | |
| **Goal**: `utd_ratio` alias for `training_iter`, demo buffer via `ReplayWithDemos` subclass. | |
| Changes: | |
| - `sac.py`: `utd_ratio` alias for `training_iter` | |
| - `replay.py`: `ReplayWithDemos` subclass (50/50 symmetric sampling from demo and online data) | |
| - Spec: `sac_mujoco_highutd_arc.yaml` (UTD=20 + layer norm critic) | |
| **Verify**: High-UTD SAC on Hopper-v5 — converge in ~50% fewer env steps vs standard SAC. | |
| ## TODO: Step 6 — Pretrained Vision Encoders | |
| **Goal**: DINOv2 encoder via torcharc, DrQ augmentation wrapper. | |
| Changes: | |
| - `pretrained.py`: `PretrainedEncoder` module (DINOv2, freeze/fine-tune, projection) | |
| - `wrappers.py`: `RandomShiftWrapper` (DrQ-v2 ±4px shift augmentation) | |
| - Spec: `ppo_vision_arc.yaml` | |
| **Verify**: PPO with DINOv2 on DMControl pixel tasks (Walker Walk, Cartpole Swingup). Frozen vs fine-tuned comparison. | |
| --- | |
| ## Environment Plan (Future) | |
| Three tiers of environment coverage: | |
| | Tier | Platform | Purpose | | |
| |------|----------|---------| | |
| | **Broad/Basic** | [Gymnasium](https://gymnasium.farama.org/) | Standard RL benchmarks (CartPole, MuJoCo, Atari) | | |
| | **Physics-rich** | [DeepMind MuJoCo Playground](https://github.com/google-deepmind/mujoco_playground) | GPU-accelerated locomotion, manipulation, dexterous tasks | | |
| | **Sim-to-real** | [NVIDIA Isaac Lab](https://github.com/isaac-sim/IsaacLab) | GPU-accelerated, sim-to-real transfer, robot learning | | |
| --- | |
| ## Key Findings | |
| - **Symlog harms model-free RL**: Tested across 6 envs — consistently hurts PPO and SAC. Designed for DreamerV3's world model, not direct value/Q-target compression. | |
| - **Layer norm is the most reliable feature**: Helps on harder envs (LunarLander +56%, Humanoid +8%), neutral on simple envs. | |
| - **CrossQ unstable on some envs**: Loss divergence on Ant and Hopper. Stable on CartPole, Humanoid. May need batch norm tuning for high-dimensional action spaces. | |
| - **Features help more on harder envs**: Simple envs (CartPole, Acrobot) — baseline wins. Complex envs (Humanoid) — v2 and CrossQ win. | |
| ## Not in Scope | |
| - World models (DreamerV3/RSSM) — Dasein Agent Phase 3.2c | |
| - Plasticity loss mitigation (CReLU, periodic resets) — future work | |
| - PPO+ principled fixes (ICLR 2025) — evaluate after base normalization stack | |