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What is Reinforcement Learning?

Taxonomy of RL Methods

Multi-Armed Bandits

Markov Decision Processes

Dynamic Programming

Monte Carlo and Temporal-Difference Prediction

Sarsa and Q-Learning

Deep Q-Networks

DQN Improvements

Policy Gradient and REINFORCE

Actor-Critic, A2C and A3C

Off-Policy Policy-Based Framework

Dyna and Learned Models

Model Predictive Control

AlphaZero and MuZero

RLHF and Language Models

Imitation Learning

Goal-Conditioned RL

Advanced TopicsMulti-Agent RL

Multi-Agent RL

Cooperative and competitive multi-agent learning, MADDPG, QMIX, and communication.

To be done soon

Goal-Conditioned RL

Hindsight Experience Replay and learning to reach arbitrary goals.

References

Books, papers, courses, notes, and code resources used while writing the handbook.