> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Reinforcement Learning

> Model-free and model-based reinforcement learning algorithms.

This chapter covers reinforcement learning algorithms for learning optimal behavior through interaction with an environment.

## Topics

<CardGroup cols={2}>
  <Card title="RL Overview" icon="gamepad" href="/aiml-common/lectures/reinforcement-learning/index">
    Introduction to reinforcement learning.
  </Card>

  <Card title="Model-Based RL" icon="cube" href="/aiml-common/lectures/reinforcement-learning/model-based-algorithms/index">
    Learning and using environment models.
  </Card>

  <Card title="Monte Carlo Control" icon="dice" href="/aiml-common/lectures/reinforcement-learning/value-based-algorithms/control/greedy-monte-carlo/index">
    Learning from complete episodes.
  </Card>

  <Card title="SARSA" icon="arrows-rotate" href="/aiml-common/lectures/reinforcement-learning/value-based-algorithms/control/sarsa/index">
    On-policy temporal difference control.
  </Card>

  <Card title="REINFORCE" icon="chart-line" href="/aiml-common/lectures/reinforcement-learning/policy-based-algorithms/reinforce/index">
    Policy gradient methods.
  </Card>
</CardGroup>

***

<Callout icon="pen-to-square" iconType="regular">
  [Edit this page on GitHub](https://github.com/aegean-ai/eaia/edit/main/src/book/reinforcement-learning/index.mdx) or [file an issue](https://github.com/aegean-ai/eaia/issues/new/choose).
</Callout>