# SLM-Lab Benchmarks

Reproducible deep RL algorithm validation across Gymnasium environments (Classic Control, Box2D, MuJoCo, Atari).

---

## Usage

After [installation](../README.md#quick-start), copy `SPEC_FILE` and `SPEC_NAME` from result tables below (Atari uses one shared spec file - see [Phase 4](#phase-4-atari)).

### Running Benchmarks

**Local** - runs on your machine (Classic Control: minutes):
```bash
slm-lab run SPEC_FILE SPEC_NAME train
```

**Remote** - cloud GPU via [dstack](https://dstack.ai), auto-syncs to HuggingFace:
```bash
source .env && slm-lab run-remote --gpu SPEC_FILE SPEC_NAME train -n NAME
```

Remote setup: `cp .env.example .env` then set `HF_TOKEN`. See [README](../README.md#cloud-training-dstack) for dstack config.

### Atari

All games share one spec file (54 tested, 5 hard exploration skipped). Use `-s env=ENV` to substitute. Runs take ~2-3 hours on GPU.

```bash
source .env && slm-lab run-remote --gpu -s env=ALE/Pong-v5 slm_lab/spec/benchmark/ppo/ppo_atari.json ppo_atari train -n pong
```

### Download Results

Trained models and metrics sync to [HuggingFace](https://huggingface.co/datasets/SLM-Lab/benchmark). Pull locally:
```bash
source .env && slm-lab pull SPEC_NAME
slm-lab list  # see available experiments
```

### Benchmark Contribution
To ensure benchmark integrity, follow these steps when adding or updating results:

#### 1. Audit Spec Settings
*   **Before Running**: Ensure `spec.json` matches the **Settings** line defined in each benchmark table.
*   **Example**: `max_frame 3e5 | num_envs 4 | max_session 4 | log_frequency 500`
*   **After Pulling**: Verify the downloaded `spec.json` matches these rules before using the data.

#### 2. Run Benchmark & Commit Specs
*   **Run**: Execute the benchmark locally or remotely using the commands in [Usage](#usage).
*   **Commit Specs**: Always commit the `spec.json` file used for the run to the repo.
*   **Table Entry**: Ensure `BENCHMARKS.md` has an entry with the correct `SPEC_FILE` and `SPEC_NAME`.

#### 3. Record Scores & Plots
*   **Score**: At run completion, extract `total_reward_ma` from logs (`trial_metrics`).
*   **Link**: Add HuggingFace folder link: `[FOLDER](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/FOLDER)`
*   **Pull data**: `source .env && uv run hf download SLM-Lab/benchmark-dev --include "data/FOLDER/*" --local-dir hf_data --repo-type dataset`
*   **Plot**: Generate with folders from table:
    ```bash
    slm-lab plot -t "CartPole-v1" -f ppo_cartpole_2026...,dqn_cartpole_2026...
    ```

### Environment Settings

Standardized settings for fair comparison. The **Settings** line in each result table shows these values.

| Env Category | num_envs | max_frame | log_frequency | grace_period |
|--------------|----------|-----------|---------------|--------------|
| Classic Control | 4 | 2e5-3e5 | 500 | 1e4 |
| Box2D | 8 | 3e5 | 1000 | 5e4 |
| MuJoCo | 16 | 1e6-10e6 | 1e4 | 1e5-1e6 |
| Atari | 16 | 10e6 | 10000 | 5e5 |

### Hyperparameter Search

When algorithm fails to reach target, run search instead of train:

```bash
slm-lab run SPEC_FILE SPEC_NAME search                                        # local
source .env && slm-lab run-remote --gpu SPEC_FILE SPEC_NAME search -n NAME    # remote
```

| Stage | Mode | Config | Purpose |
|-------|------|--------|---------|
| ASHA | `search` | `max_session=1`, `search_scheduler` enabled | Wide exploration with early stopping |
| Multi | `search` | `max_session=4`, NO `search_scheduler` | Robust validation with averaging |
| Validate | `train` | Final spec | Confirmation run |

> Do not use search result in benchmark results - use final validation run with committed spec.

Search budget: ~3-4 trials per dimension (8 trials = 2-3 dims, 16 = 3-4 dims, 20+ = 5+ dims).

```json
{
  "meta": {
    "max_session": 1, "max_trial": 16,
    "search_resources": {"cpu": 1, "gpu": 0.125},
    "search_scheduler": {"grace_period": 1e5, "reduction_factor": 3}
  },
  "search": {
    "agent.algorithm.gamma__uniform": [0.98, 0.999],
    "agent.algorithm.lam__uniform": [0.9, 0.98],
    "agent.net.optim_spec.lr__loguniform": [1e-4, 1e-3]
  }
}
```



## Progress

| Phase | Category | Envs | REINFORCE | SARSA | DQN | DDQN+PER | A2C | PPO | SAC | Overall |
|-------|----------|------|-----------|-------|-----|----------|-----|-----|-----|---------|
| 1 | Classic Control | 3 | 🔄 | 🔄 | 🔄 | 🔄 | 🔄 | 🔄 | 🔄 | Rerun pending |
| 2 | Box2D | 2 | N/A | N/A | 🔄 | 🔄 | 🔄 | 🔄 | 🔄 | Rerun pending |
| 3 | MuJoCo | 11 | N/A | N/A | N/A | N/A | 🔄 | 🔄 | 🔄 | Rerun pending |
| 4 | Atari | 59 | N/A | N/A | Skip | Skip | Skip | 🔄 | N/A | **54 games** (not in this rerun) |

**Legend**: ✅ Solved | ⚠️ Close (>80%) | 📊 Acceptable | ❌ Failed | 🔄 In progress/Pending | Skip Not started | N/A Not applicable

---

## Results

### Phase 1: Classic Control

#### 1.1 CartPole-v1

**Docs**: [CartPole](https://gymnasium.farama.org/environments/classic_control/cart_pole/) | State: Box(4) | Action: Discrete(2) | Target reward MA > 400

**Settings**: max_frame 2e5 | num_envs 4 | max_session 4 | log_frequency 500

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| REINFORCE | ✅ | 469.68 | [slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json](../slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json) | reinforce_cartpole | [reinforce_cartpole_2026_01_30_215510](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/reinforce_cartpole_2026_01_30_215510) |
| SARSA | ✅ | 421.58 | [slm_lab/spec/benchmark/sarsa/sarsa_cartpole.json](../slm_lab/spec/benchmark/sarsa/sarsa_cartpole.json) | sarsa_boltzmann_cartpole | [sarsa_boltzmann_cartpole_2026_01_30_215508](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sarsa_boltzmann_cartpole_2026_01_30_215508) |
| DQN | ⚠️ | 188.07 | [slm_lab/spec/benchmark/dqn/dqn_cartpole.json](../slm_lab/spec/benchmark/dqn/dqn_cartpole.json) | dqn_boltzmann_cartpole | [dqn_boltzmann_cartpole_2026_01_30_215213](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/dqn_boltzmann_cartpole_2026_01_30_215213) |
| DDQN+PER | ✅ | 432.88 | [slm_lab/spec/benchmark/dqn/dqn_cartpole.json](../slm_lab/spec/benchmark/dqn/dqn_cartpole.json) | ddqn_per_boltzmann_cartpole | [ddqn_per_boltzmann_cartpole_2026_01_30_215454](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ddqn_per_boltzmann_cartpole_2026_01_30_215454) |
| A2C | ✅ | 499.73 | [slm_lab/spec/benchmark/a2c/a2c_gae_cartpole.json](../slm_lab/spec/benchmark/a2c/a2c_gae_cartpole.json) | a2c_gae_cartpole | [a2c_gae_cartpole_2026_01_30_215337](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_cartpole_2026_01_30_215337) |
| PPO | ✅ | 499.54 | [slm_lab/spec/benchmark/ppo/ppo_cartpole.json](../slm_lab/spec/benchmark/ppo/ppo_cartpole.json) | ppo_cartpole | [ppo_cartpole_2026_01_30_221924](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_cartpole_2026_01_30_221924) |
| SAC | ⚠️ | 359.69 | [slm_lab/spec/benchmark/sac/sac_cartpole.json](../slm_lab/spec/benchmark/sac/sac_cartpole.json) | sac_cartpole | [sac_cartpole_2026_01_30_221934](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_cartpole_2026_01_30_221934) |

![CartPole-v1 Multi-Trial Graph](plots/CartPole-v1_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 1.2 Acrobot-v1

**Docs**: [Acrobot](https://gymnasium.farama.org/environments/classic_control/acrobot/) | State: Box(6) | Action: Discrete(3) | Target reward MA > -100

**Settings**: max_frame 3e5 | num_envs 4 | max_session 4 | log_frequency 500

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| DQN | ✅ | -94.81 | [slm_lab/spec/benchmark/dqn/dqn_acrobot.json](../slm_lab/spec/benchmark/dqn/dqn_acrobot.json) | dqn_boltzmann_acrobot | [dqn_boltzmann_acrobot_2026_01_30_215429](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/dqn_boltzmann_acrobot_2026_01_30_215429) |
| DDQN+PER | ✅ | -85.17 | [slm_lab/spec/benchmark/dqn/ddqn_per_acrobot.json](../slm_lab/spec/benchmark/dqn/ddqn_per_acrobot.json) | ddqn_per_acrobot | [ddqn_per_acrobot_2026_01_30_215436](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ddqn_per_acrobot_2026_01_30_215436) |
| A2C | ✅ | -83.75 | [slm_lab/spec/benchmark/a2c/a2c_gae_acrobot.json](../slm_lab/spec/benchmark/a2c/a2c_gae_acrobot.json) | a2c_gae_acrobot | [a2c_gae_acrobot_2026_01_30_215413](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_acrobot_2026_01_30_215413) |
| PPO | ✅ | -81.43 | [slm_lab/spec/benchmark/ppo/ppo_acrobot.json](../slm_lab/spec/benchmark/ppo/ppo_acrobot.json) | ppo_acrobot | [ppo_acrobot_2026_01_30_215352](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_acrobot_2026_01_30_215352) |
| SAC | ✅ | -97.08 | [slm_lab/spec/benchmark/sac/sac_acrobot.json](../slm_lab/spec/benchmark/sac/sac_acrobot.json) | sac_acrobot | [sac_acrobot_2026_01_30_215401](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_acrobot_2026_01_30_215401) |

![Acrobot-v1 Multi-Trial Graph](plots/Acrobot-v1_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 1.3 Pendulum-v1

**Docs**: [Pendulum](https://gymnasium.farama.org/environments/classic_control/pendulum/) | State: Box(3) | Action: Box(1) | Target reward MA > -200

**Settings**: max_frame 3e5 | num_envs 4 | max_session 4 | log_frequency 500

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| A2C | ❌ | -553.00 | [slm_lab/spec/benchmark/a2c/a2c_gae_pendulum.json](../slm_lab/spec/benchmark/a2c/a2c_gae_pendulum.json) | a2c_gae_pendulum | [a2c_gae_pendulum_2026_01_30_215421](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_pendulum_2026_01_30_215421) |
| PPO | ✅ | -168.26 | [slm_lab/spec/benchmark/ppo/ppo_pendulum.json](../slm_lab/spec/benchmark/ppo/ppo_pendulum.json) | ppo_pendulum | [ppo_pendulum_2026_01_30_215944](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_pendulum_2026_01_30_215944) |
| SAC | ✅ | -152.33 | [slm_lab/spec/benchmark/sac/sac_pendulum.json](../slm_lab/spec/benchmark/sac/sac_pendulum.json) | sac_pendulum | [sac_pendulum_2026_01_30_215454](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_pendulum_2026_01_30_215454) |

![Pendulum-v1 Multi-Trial Graph](plots/Pendulum-v1_multi_trial_graph_mean_returns_ma_vs_frames.png)

### Phase 2: Box2D

#### 2.1 LunarLander-v3 (Discrete)

**Docs**: [LunarLander](https://gymnasium.farama.org/environments/box2d/lunar_lander/) | State: Box(8) | Action: Discrete(4) | Target reward MA > 200

**Settings**: max_frame 3e5 | num_envs 8 | max_session 4 | log_frequency 1000

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| DQN | ⚠️ | 183.64 | [slm_lab/spec/benchmark/dqn/dqn_lunar.json](../slm_lab/spec/benchmark/dqn/dqn_lunar.json) | dqn_concat_lunar | [dqn_concat_lunar_2026_01_30_215529](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/dqn_concat_lunar_2026_01_30_215529) |
| DDQN+PER | ✅ | 261.49 | [slm_lab/spec/benchmark/dqn/ddqn_per_lunar.json](../slm_lab/spec/benchmark/dqn/ddqn_per_lunar.json) | ddqn_per_concat_lunar | [ddqn_per_concat_lunar_2026_01_30_215532](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ddqn_per_concat_lunar_2026_01_30_215532) |
| A2C | ❌ | 9.53 | [slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json](../slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json) | a2c_gae_lunar | [a2c_gae_lunar_2026_01_30_215529](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_lunar_2026_01_30_215529) |
| PPO | ⚠️ | 159.02 | [slm_lab/spec/benchmark/ppo/ppo_lunar.json](../slm_lab/spec/benchmark/ppo/ppo_lunar.json) | ppo_lunar | [ppo_lunar_2026_01_30_215550](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_lunar_2026_01_30_215550) |
| SAC | ❌ | -75.43 | [slm_lab/spec/benchmark/sac/sac_lunar.json](../slm_lab/spec/benchmark/sac/sac_lunar.json) | sac_lunar | [sac_lunar_2026_01_30_215552](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_lunar_2026_01_30_215552) |

![LunarLander-v3 (Discrete) Multi-Trial Graph](plots/LunarLander-v3_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 2.2 LunarLander-v3 (Continuous)

**Docs**: [LunarLander](https://gymnasium.farama.org/environments/box2d/lunar_lander/) | State: Box(8) | Action: Box(2) | Target reward MA > 200

**Settings**: max_frame 3e5 | num_envs 8 | max_session 4 | log_frequency 1000

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| A2C | ❌ | -38.18 | [slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json](../slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json) | a2c_gae_lunar_continuous | [a2c_gae_lunar_continuous_2026_01_30_215630](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_lunar_continuous_2026_01_30_215630) |
| PPO | ⚠️ | 165.48 | [slm_lab/spec/benchmark/ppo/ppo_lunar.json](../slm_lab/spec/benchmark/ppo/ppo_lunar.json) | ppo_lunar_continuous | [ppo_lunar_continuous_2026_01_31_104549](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_lunar_continuous_2026_01_31_104549) |
| SAC | ✅ | 208.60 | [slm_lab/spec/benchmark/sac/sac_lunar.json](../slm_lab/spec/benchmark/sac/sac_lunar.json) | sac_lunar_continuous | [sac_lunar_continuous_2026_01_31_104537](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_lunar_continuous_2026_01_31_104537) |

![LunarLander-v3 (Continuous) Multi-Trial Graph](plots/LunarLander-v3_Continuous_multi_trial_graph_mean_returns_ma_vs_frames.png)

### Phase 3: MuJoCo

**Docs**: [MuJoCo environments](https://gymnasium.farama.org/environments/mujoco/) | State/Action: Continuous | Target: Practical baselines (no official "solved" threshold)

**Settings**: max_frame 4e6-10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

**Algorithm: PPO only** - SAC omitted (off-policy = heavy compute for systematic benchmarking). Network: MLP [256,256] tanh, orthogonal init.

**Spec Variants**: Two unified specs in [ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json), plus individual specs for edge cases.

| SPEC_NAME | Envs | Key Config |
|-----------|------|------------|
| ppo_mujoco | HalfCheetah, Walker, Humanoid, HumanoidStandup | gamma=0.99, lam=0.95 |
| ppo_mujoco_longhorizon | Reacher, Pusher | gamma=0.997, lam=0.97 |
| Individual specs | Hopper, Swimmer, Ant, IP, IDP | See spec files for tuned hyperparams |

**Reproduce**: Copy `ENV`, `SPEC_FILE`, `SPEC_NAME` from table. Use `-s max_frame=` for all specs, add `-s env=` for unified specs:
```bash
# Unified specs (ppo_mujoco.json)
source .env && slm-lab run-remote --gpu -s env=ENV -s max_frame=MAX_FRAME \
  slm_lab/spec/benchmark/ppo/ppo_mujoco.json SPEC_NAME train -n NAME

# Individual specs (env hardcoded)
source .env && slm-lab run-remote --gpu -s max_frame=MAX_FRAME \
  slm_lab/spec/benchmark/ppo/SPEC_FILE SPEC_NAME train -n NAME
```

| ENV | MAX_FRAME | SPEC_FILE | SPEC_NAME |
|-----|-----------|-----------|-----------|
| HalfCheetah-v5 | 10e6 | ppo_mujoco.json | ppo_mujoco |
| Walker2d-v5 | 10e6 | ppo_mujoco.json | ppo_mujoco |
| Humanoid-v5 | 10e6 | ppo_mujoco.json | ppo_mujoco |
| HumanoidStandup-v5 | 4e6 | ppo_mujoco.json | ppo_mujoco |
| Hopper-v5 | 4e6 | ppo_hopper.json | ppo_hopper |
| Swimmer-v5 | 4e6 | ppo_swimmer.json | ppo_swimmer |
| Ant-v5 | 10e6 | ppo_ant.json | ppo_ant |
| Reacher-v5 | 4e6 | ppo_mujoco.json | ppo_mujoco_longhorizon |
| Pusher-v5 | 4e6 | ppo_mujoco.json | ppo_mujoco_longhorizon |
| InvertedPendulum-v5 | 4e6 | ppo_inverted_pendulum.json | ppo_inverted_pendulum |
| InvertedDoublePendulum-v5 | 10e6 | ppo_inverted_double_pendulum.json | ppo_inverted_double_pendulum |

#### 3.1 Hopper-v5

**Docs**: [Hopper](https://gymnasium.farama.org/environments/mujoco/hopper/) | State: Box(11) | Action: Box(3) | Target reward MA > 2000

**Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ⚠️ | 1174.57 | [slm_lab/spec/benchmark/ppo/ppo_hopper.json](../slm_lab/spec/benchmark/ppo/ppo_hopper.json) | ppo_hopper | [ppo_hopper_2026_01_30_220138](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_hopper_2026_01_30_220138) |

![Hopper-v5 Multi-Trial Graph](plots/Hopper-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.2 HalfCheetah-v5

**Docs**: [HalfCheetah](https://gymnasium.farama.org/environments/mujoco/half_cheetah/) | State: Box(17) | Action: Box(6) | Target reward MA > 5000

**Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | 5851.70 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco | [ppo_mujoco_halfcheetah_2026_01_30_230302](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_mujoco_halfcheetah_2026_01_30_230302) |

![HalfCheetah-v5 Multi-Trial Graph](plots/HalfCheetah-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.3 Walker2d-v5

**Docs**: [Walker2d](https://gymnasium.farama.org/environments/mujoco/walker2d/) | State: Box(17) | Action: Box(6) | Target reward MA > 3500

**Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | 4042.07 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco | [ppo_mujoco_walker2d_2026_01_30_222124](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_mujoco_walker2d_2026_01_30_222124) |

![Walker2d-v5 Multi-Trial Graph](plots/Walker2d-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.4 Ant-v5

**Docs**: [Ant](https://gymnasium.farama.org/environments/mujoco/ant/) | State: Box(105) | Action: Box(8) | Target reward MA > 2000

**Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | 2514.64 | [slm_lab/spec/benchmark/ppo/ppo_ant.json](../slm_lab/spec/benchmark/ppo/ppo_ant.json) | ppo_ant | [ppo_ant_2026_01_31_042006](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_ant_2026_01_31_042006) |

![Ant-v5 Multi-Trial Graph](plots/Ant-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.5 Swimmer-v5

**Docs**: [Swimmer](https://gymnasium.farama.org/environments/mujoco/swimmer/) | State: Box(8) | Action: Box(2) | Target reward MA > 200

**Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | 229.31 | [slm_lab/spec/benchmark/ppo/ppo_swimmer.json](../slm_lab/spec/benchmark/ppo/ppo_swimmer.json) | ppo_swimmer | [ppo_swimmer_2026_01_30_215922](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_swimmer_2026_01_30_215922) |

![Swimmer-v5 Multi-Trial Graph](plots/Swimmer-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.6 Reacher-v5

**Docs**: [Reacher](https://gymnasium.farama.org/environments/mujoco/reacher/) | State: Box(11) | Action: Box(2) | Target reward MA > -10

**Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | -5.08 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco_longhorizon | [ppo_mujoco_longhorizon_reacher_2026_01_30_215805](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_mujoco_longhorizon_reacher_2026_01_30_215805) |

![Reacher-v5 Multi-Trial Graph](plots/Reacher-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.7 Pusher-v5

**Docs**: [Pusher](https://gymnasium.farama.org/environments/mujoco/pusher/) | State: Box(23) | Action: Box(7) | Target reward MA > -50

**Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | -49.09 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco_longhorizon | [ppo_mujoco_longhorizon_pusher_2026_01_30_215824](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_mujoco_longhorizon_pusher_2026_01_30_215824) |

![Pusher-v5 Multi-Trial Graph](plots/Pusher-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.8 InvertedPendulum-v5

**Docs**: [InvertedPendulum](https://gymnasium.farama.org/environments/mujoco/inverted_pendulum/) | State: Box(4) | Action: Box(1) | Target reward MA ~1000

**Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | 944.87 | [slm_lab/spec/benchmark/ppo/ppo_inverted_pendulum.json](../slm_lab/spec/benchmark/ppo/ppo_inverted_pendulum.json) | ppo_inverted_pendulum | [ppo_inverted_pendulum_2026_01_30_230211](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_inverted_pendulum_2026_01_30_230211) |

![InvertedPendulum-v5 Multi-Trial Graph](plots/InvertedPendulum-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.9 InvertedDoublePendulum-v5

**Docs**: [InvertedDoublePendulum](https://gymnasium.farama.org/environments/mujoco/inverted_double_pendulum/) | State: Box(11) | Action: Box(1) | Target reward MA ~8000

**Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | 7622.00 | [slm_lab/spec/benchmark/ppo/ppo_inverted_double_pendulum.json](../slm_lab/spec/benchmark/ppo/ppo_inverted_double_pendulum.json) | ppo_inverted_double_pendulum | [ppo_inverted_double_pendulum_2026_01_30_220651](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_inverted_double_pendulum_2026_01_30_220651) |

![InvertedDoublePendulum-v5 Multi-Trial Graph](plots/InvertedDoublePendulum-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.10 Humanoid-v5

**Docs**: [Humanoid](https://gymnasium.farama.org/environments/mujoco/humanoid/) | State: Box(376) | Action: Box(17) | Target reward MA > 1000

**Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | 3774.08 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco | [ppo_mujoco_humanoid_2026_01_30_222339](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_mujoco_humanoid_2026_01_30_222339) |

![Humanoid-v5 Multi-Trial Graph](plots/Humanoid-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

#### 3.11 HumanoidStandup-v5

**Docs**: [HumanoidStandup](https://gymnasium.farama.org/environments/mujoco/humanoid_standup/) | State: Box(376) | Action: Box(17) | Target reward MA > 100000

**Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
|-----------|--------|-----|-----------|-----------|---------|
| PPO | ✅ | 165841.17 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco | [ppo_mujoco_humanoidstandup_2026_01_30_215802](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/ppo_mujoco_humanoidstandup_2026_01_30_215802) |

![HumanoidStandup-v5 Multi-Trial Graph](plots/HumanoidStandup-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)

### Phase 4: Atari

**Docs**: [Atari environments](https://ale.farama.org/environments/) | State: Box(84,84,4 after preprocessing) | Action: Discrete(4-18, game-dependent) | Solved: Game-specific thresholds

**Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 10000

**Environment**:
- Gymnasium ALE v5 with `life_loss_info=true`
- v5 is harder than v4 due to sticky actions (default `repeat_action_probability=0.25` vs v4's 0.0), which randomly repeats agent actions to simulate console stochasticity and prevent memorization, following [Machado et al. (2018)](https://arxiv.org/abs/1709.06009) research best practices. See [ALE version history](https://ale.farama.org/environments/#version-history-and-naming-schemes).

**Algorithm: PPO**:
- **Network**: ConvNet [32,64,64] + 512fc (Nature CNN), orthogonal init, normalize=true, clip_grad_val=0.5
- **Hyperparams**: AdamW (lr=2.5e-4, eps=1e-5), minibatch_size=256, time_horizon=128, training_epoch=4, clip_eps=0.1, entropy_coef=0.01

**Lambda Variants**: All use one spec file ([slm_lab/spec/benchmark/ppo/ppo_atari.json](../slm_lab/spec/benchmark/ppo/ppo_atari.json)), differing only in GAE lambda. Lower lambda = bias toward immediate rewards (action games), higher = longer credit horizon (strategic games).

| SPEC_NAME | Lambda | Best for |
|-----------|--------|----------|
| ppo_atari | 0.95 | Long-horizon, strategic games (default) |
| ppo_atari_lam85 | 0.85 | Mixed/moderate games |
| ppo_atari_lam70 | 0.70 | Fast action games |

**Reproduce**: Copy `ENV` from first column, `SPEC_NAME` from column header. All use the same SPEC_FILE:
```bash
source .env && slm-lab run-remote --gpu -s env=ENV \
  slm_lab/spec/benchmark/ppo/ppo_atari.json SPEC_NAME train -n NAME
```

| ENV\SPEC_NAME | ppo_atari | ppo_atari_lam85 | ppo_atari_lam70 |
| -------- | ----------------- | --------------- | --------------- |
| ALE/Adventure-v5 | Skip | Skip | Skip |
| ALE/AirRaid-v5 | **8245** | - | - |
| ALE/Alien-v5 | **1453** | 1353 | 1274 |
| ALE/Amidar-v5 | 574 | **580** | - |
| ALE/Assault-v5 | 4059 | **4293** | 3314 |
| ALE/Asterix-v5 | 2967 | **3482** | - |
| ALE/Asteroids-v5 | 1497 | **1554** | - |
| ALE/Atlantis-v5 | **792886** | 754k | 710k |
| ALE/BankHeist-v5 | **1045** | 1045 | - |
| ALE/BattleZone-v5 | 21270 | **26383** | 13857 |
| ALE/BeamRider-v5 | **2765** | - | - |
| ALE/Berzerk-v5 | **1072** | - | - |
| ALE/Bowling-v5 | **46.45** | - | - |
| ALE/Boxing-v5 | **91.17** | - | - |
| ALE/Breakout-v5 | 191 | 292 | **327** |
| ALE/Carnival-v5 | 3071 | 3013 | **3967** |
| ALE/Centipede-v5 | 3917 | - | **4915** |
| ALE/ChopperCommand-v5 | **5355** | - | - |
| ALE/CrazyClimber-v5 | 107183 | **107370** | - |
| ALE/Defender-v5 | 37162 | - | **51439** |
| ALE/DemonAttack-v5 | 7755 | - | **16558** |
| ALE/DoubleDunk-v5 | **-2.38** | - | - |
| ALE/ElevatorAction-v5 | **5446** | 363 | 3933 |
| ALE/Enduro-v5 | 414 | **898** | 872 |
| ALE/FishingDerby-v5 | 22.80 | **27.10** | - |
| ALE/Freeway-v5 | **31.30** | - | - |
| ALE/Frostbite-v5 | **301** | 275 | 267 |
| ALE/Gopher-v5 | 4172 | - | **6508** |
| ALE/Gravitar-v5 | **599** | 253 | 145 |
| ALE/Hero-v5 | 21052 | **28238** | - |
| ALE/IceHockey-v5 | **-3.93** | -5.58 | -7.36 |
| ALE/Jamesbond-v5 | **662** | - | - |
| ALE/JourneyEscape-v5 | -1582 | **-1252** | -1547 |
| ALE/Kangaroo-v5 | 2623 | **9912** | - |
| ALE/Krull-v5 | **7841** | - | - |
| ALE/KungFuMaster-v5 | 18973 | 28334 | **29068** |
| ALE/MontezumaRevenge-v5 | Skip | Skip | Skip |
| ALE/MsPacman-v5 | 2308 | **2372** | 2297 |
| ALE/NameThisGame-v5 | **5993** | - | - |
| ALE/Phoenix-v5 | 7940 | - | **15659** |
| ALE/Pitfall-v5 | Skip | Skip | Skip |
| ALE/Pong-v5 | 15.01 | **16.91** | 12.85 |
| ALE/Pooyan-v5 | 4704 | - | **5716** |
| ALE/PrivateEye-v5 | Skip | Skip | Skip |
| ALE/Qbert-v5 | **15094** | - | - |
| ALE/Riverraid-v5 | 7319 | **9428** | - |
| ALE/RoadRunner-v5 | 24204 | **37015** | - |
| ALE/Robotank-v5 | **20.07** | 8.24 | 2.59 |
| ALE/Seaquest-v5 | **1796** | - | - |
| ALE/Skiing-v5 | **-19340** | -22980 | -29975 |
| ALE/Solaris-v5 | **2094** | - | - |
| ALE/SpaceInvaders-v5 | **726** | - | - |
| ALE/StarGunner-v5 | 31862 | - | **47495** |
| ALE/Surround-v5 | **-2.52** | - | -6.79 |
| ALE/Tennis-v5 | -7.66 | **-4.41** | - |
| ALE/TimePilot-v5 | **4668** | - | - |
| ALE/Tutankham-v5 | 203 | **217** | - |
| ALE/UpNDown-v5 | **182472** | - | - |
| ALE/Venture-v5 | Skip | Skip | Skip |
| ALE/VideoPinball-v5 | 31385 | - | **56746** |
| ALE/WizardOfWor-v5 | **5814** | 5466 | 4740 |
| ALE/YarsRevenge-v5 | **17120** | - | - |
| ALE/Zaxxon-v5 | **10756** | - | - |

**Legend**: **Bold** = Best score | Skip = Hard exploration | - = Not tested

---

#### Sticky Actions Validation (v5 vs v4-style)

Testing hypothesis that lower scores are due to sticky actions (`repeat_action_probability=0.25` in v5 vs `0.0` in v4/CleanRL).

**Environment**: Same as above, but with `repeat_action_probability=0.0` (matching CleanRL/old v4 behavior)

**Reproduce**: Copy `ENV` from first column:
```bash
source .env && slm-lab run-remote --gpu -s env=ENV \
  slm_lab/spec/benchmark/ppo/ppo_atari.json ppo_atari_nosticky train -n NAME
```

**Results** (Testing games with significant regression):

| ENV | v5 (sticky=0.25) | v4-style (sticky=0.0) | Diff | % Change |
| --- | ---------------- | --------------------- | ---- | -------- |
| ALE/Skiing-v5 | -19340 | - | - | - |
| ALE/Frostbite-v5 | 301 | - | - | - |
| ALE/ElevatorAction-v5 | 5446 | - | - | - |
| ALE/Gravitar-v5 | 599 | - | - | - |
| ALE/WizardOfWor-v5 | 5814 | - | - | - |
| ALE/Alien-v5 | 1453 | - | - | - |
| ALE/KungFuMaster-v5 | 29068 | - | - | - |
| ALE/Atlantis-v5 | 792886 | - | - | - |
| ALE/Pong-v5 | 15.01 | - | - | - |
| ALE/Breakout-v5 | 191 | - | - | - |