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name: reinforcement-learning description: "Routes TensorLayer reinforcement-learning utilities and tutorial workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Reinforcement Learning

Use this sub-skill for TensorLayer reward utilities, action sampling, and RL tutorial guidance. This is the route for RL helper functions and reference tutorials that sit around the core model APIs.

Typical requests

• Discount episode rewards.
• Sample an action from a probability distribution.
• Inspect DQN or Q-learning tutorial structure.
• Understand what TensorLayer's RL examples require from Gym or related extras.

Read first

• references/rl-reference.md for the utility surface and tutorial notes.
• references/workflows.md for tiny reward-discount and action-sampling patterns.
• references/troubleshooting.md for Gym, stochasticity, and long-running-example issues.

Bundled check

• scripts/smoke_rl.py verifies deterministic reward discounting and a simple action-selection path.

Boundaries

Include here:

• tensorlayer.rein
• RL reward and action helpers
• RL tutorial guidance and dependency notes

Exclude or route elsewhere:

• core layer/model definitions -> core-modeling
• supervised training loops and CLI help -> training-and-cli
• preprocessing, TFRecord, and iteration helpers -> data-and-utilities
• text, seq2seq, or vision workflows -> text-and-sequence / vision-and-apps

Fast path

1. Decide whether the user needs a utility function or a full RL tutorial reference.
2. Keep Gym-based tutorials reference-only unless the user explicitly requests the extra runtime stack.
3. Use the smoke script to verify the reward helpers before opening the longer tutorials.