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name: evaluation description: "Evaluate pretrained PPO checkpoints, resolve checkpoint/environment configuration, and diagnose load or render issues." metadata: disco-role: operating disable-model-invocation: true license: MIT

Evaluation

Use this sub-skill when the user asks to test a pretrained network, evaluate a checkpoint, render a trained policy, average episode rewards, or understand why a PPO checkpoint load fails.

Do not use this sub-skill for training-loop changes, PPO update debugging, reward-log plotting, or GIF composition. Route log plots and frame/GIF workflows to the sibling visualization sub-skill. Route low-level PPO.load, PPO.save, select_action, and architecture questions to the root shared API reference.

What this sub-skill owns

• Resolving the pretrained checkpoint path convention: PPO_preTrained/<env_name>/PPO_<env_name>_<random_seed>_<run_num>.pth.
• Matching checkpoint names to environment names, action-space class, and PPO constructor dimensions.
• Preserving continuous-policy action_std alignment; the repo's checkpoint files do not store action_std.
• Running or adapting the test.py evaluation pattern: load policy, run episodes, print per-episode rewards, and report the average reward.
• Deciding whether rendering is safe in local, notebook, remote, or headless sessions.
• Diagnosing missing Gym/Roboschool dependencies, checkpoint path mistakes, Gym/Gymnasium API drift, and state-dict shape mismatches.

Quick workflow

1. Resolve the intended checkpoint before running an environment. Use the bundled helper from this sub-skill directory:

   python scripts/evaluation_config_helper.py --list-presets
   python scripts/evaluation_config_helper.py \
     --env-name RoboschoolWalker2d-v1 \
     --checkpoint-root PPO_preTrained \
     --check-file
   

   The helper is path/configuration-only by default. It does not import gym, roboschool, or the PPO module and does not run rollouts.

2. Match the live environment to the checkpoint. The environment used for gym.make(env_name) must expose the same observation dimension, action dimension, and action-space type that were used to save the state dict. A checkpoint named for CartPole-v1 is not load-compatible with LunarLander-v2, and a discrete checkpoint is not compatible with a continuous Box policy.

3. Construct the PPO agent with evaluation-compatible arguments. The original testing script uses lr_actor=0.0003, lr_critic=0.001, gamma=0.99, eps_clip=0.2, and K_epochs=80. These update hyperparameters are mostly inert during pure select_action evaluation, but the constructor still requires them. Continuous policies also require an action_std float at construction time.

4. Load the checkpoint once the dimensions are known. The shared PPO implementation saves policy_old.state_dict() and load(checkpoint_path) loads the same state dict into both policy_old and policy using torch.load(..., map_location=...). Use the root API reference for exact load/save behavior.

5. Run episodes and average rewards. The native evaluation loop resets the env, calls ppo_agent.select_action(state), steps the env until done or max_ep_len, clears the rollout buffer after each episode, prints episode reward, and reports average test reward over total_test_episodes.

6. Render only when the display path is ready. Numeric evaluation does not require rendering. For on-screen display, use a compatible Gym render path and a real display. For rgb_array frame capture and GIF assembly, switch to visualization.

Core references

• Evaluation workflow - evaluation loop adaptation, old/new Gym API handling, and render choices.
• Checkpoints and environments - file naming, built-in pretrained environment presets, and action-space matching rules.
• Troubleshooting - missing dependencies, checkpoint mismatch, headless rendering, and Gym/Gymnasium compatibility failures.
• Root PPO API reference - constructor, select_action, save, and load details shared across sub-skills.

Validation commands

Safe checks for this sub-skill should avoid long rollouts unless the user explicitly has the environment packages, display, and checkpoint ready:

python scripts/evaluation_config_helper.py --help
python scripts/evaluation_config_helper.py --env-name CartPole-v1 --checkpoint-root PPO_preTrained --check-file
python -m py_compile scripts/evaluation_config_helper.py

A real episode rollout is optional and dependency-bound because the native test.py imports legacy gym and roboschool; do not treat a missing optional environment package as a failure of the helper or documentation.