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docs/BENCHMARKS.md

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+# 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-dev/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-dev/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-dev/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-dev/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-dev/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-dev/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-dev/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-dev/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 | - | - | - |
+