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name: bandits-and-reinforcement-learning description: "Routes numpy-ml bandit, policy, trainer, and optional Gym-based reinforcement-learning tasks with dependency guidance." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Bandits and Reinforcement Learning

Use this sub-skill for numpy-ml multi-armed bandit and tabular reinforcement learning workflows:

• Bernoulli, multinomial, Gaussian, contextual Bernoulli, and contextual linear bandits;
• epsilon-greedy, UCB1, Thompson sampling, and LinUCB policies;
• bandit training/comparison helpers;
• Gym-backed RL agents and trainers for Monte Carlo, TD, cross-entropy, and Dyna-style workflows.

First Checks

1. Run the plotting-free smoke helper for base bandit functionality:

   python sub-skills/bandits-and-reinforcement-learning/scripts/bandit_rl_smoke.py
   

2. Read references/api-reference.md before constructing bandits, policies, or RL agents.

3. Read references/workflows.md for a tiny bandit policy loop and an RL/Gym preparation checklist.

4. Read references/troubleshooting.md when gym, matplotlib, action-space assumptions, plotting, or runtime length block a task.

Route by Task

User asks for	Use this route
simulate a small bandit	bandit API and smoke script.
compare policies without plotting	workflow reference with plot=False.
train on an OpenAI Gym environment	RL setup checklist and troubleshooting.
debug missing gym or matplotlib warnings	troubleshooting reference.
neural-network policies or deep RL	this repo only provides educational tabular/linear-style components; route neural layers to ../neural-network-components/SKILL.md.

Operating Notes

• Bandit APIs work in the base CPU environment.
• RL training on real environments requires the optional Gym dependency and may be sensitive to Gym/Gymnasium API version drift.
• policy.act(bandit, context=None) returns (reward, arm_id) and mutates the policy's estimates.
• Plotting helpers are optional and should not be part of a safe smoke unless the user explicitly asks for plots.