reinforcement-learning-guide

npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill reinforcement-learning-guide --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Reinforcement Learning Guide Understand and implement reinforcement learning algorithms from tabular methods through deep RL, including policy gradients, actor-critic, and model-based approaches. ## RL Fundamentals ### The RL Framework An agent interacts with an environment to maximize cumulative reward: ``` Agent Environment | | |--- action a_t ---------->| | |--- next state s_{t+1} |<-- reward r_t, state s_t |--- reward r_{t+1} | | ``` | Concept | Symbol | Definition | |---------|--------|-----------| | State | s | Observation of the environment | | Action | a | Decision made by the agent | | Reward | r | Scalar feedback signal | | Policy | pi(a\|s) | Mapping from states to actions | | Value function | V(s) | Expected cumulative reward from state s | | Q-function | Q(s, a) | Expected cumulative reward from (s, a) | | Discount factor | gamma | Weight of future vs. immediate rewards (0-1) | | Return | G_t | Sum of discounted future rewards from time t | ### Key Equations ``` # Return (discounted cumulative reward) G_t = r_t + gamma * r_{t+1} + gamma^2 * r_{t+2} + ... # Bellman equation for V V(s) = E[r + gamma * V(s') | s] # Bellman equation for Q Q(s, a) = E[r + gamma * max_a' Q