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reinforcement-learning

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Understand and apply reinforcement learning — MDPs, value and policy methods, exploration, reward design, and RLHF basics. Use when decisions are sequential and feedback comes as rewards.

skills/reinforcement-learning/SKILL.md

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# Reinforcement Learning

Reinforcement learning trains agents to make sequences of decisions by rewarding good outcomes. 
It's the framework behind game-playing breakthroughs, robotics control, and the human-feedback 
tuning of language models — powerful where feedback is sequential, tricky everywhere else.

## Overview

The setup: an agent observes states, takes actions, receives rewards, and learns a policy 
maximizing long-term return. The challenges are fundamental — credit assignment (which action 
deserved the reward?), exploration vs. exploitation, and sample efficiency. In practice, RL shines 
in simulators and games; in the real world, reward design and safety dominate. RLHF (learning from 
human preferences) brought RL to language models by replacing hand-designed rewards with learned 
preference models.
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