# Reinforcement Learning

> MDPs, Q-learning, DQN, policy gradients, and multi-agent RL — from theory to game-playing and robotics.

![Status: complete](https://img.shields.io/badge/status-complete-brightgreen)

Open [`25-reinforcement-learning.ipynb`](./25-reinforcement-learning.ipynb) for learning objectives, prerequisites, and recommended reading order.

## Topics Covered

- Markov Decision Processes and Bellman equations
- Value-based methods: Q-learning, SARSA, DQN
- Policy-based methods: REINFORCE, A2C, PPO
- Multi-agent RL, hierarchical RL, and inverse RL

## Prerequisites

[06-neural-networks/README.md](../06-neural-networks/README.md) · [03-maths/README.md](../03-maths/README.md)

## What Comes Next

[26-time-series-analysis/README.md](../26-time-series-analysis/README.md)
