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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 TopicsExploration

Exploration

Curiosity-driven exploration with Random Network Distillation and Intrinsic Curiosity Module.

To be done soon

Offline RL

Learning from fixed datasets without environment interaction.

Goal-Conditioned RL

Hindsight Experience Replay and learning to reach arbitrary goals.