What is Reinforcement Learning?Taxonomy of RL Methods History of Reinforcement Learning

Multi-Armed Bandits MDP and Dynamic Programming Monte Carlo and Temporal Difference Q-Learning DQN DQN Improvements

Policy Gradient Theorem and REINFORCE TRPO PPO RL for Sequence Generation and RLHF

Actor-Critic Framework DDPG TD3 and SAC

Dyna and Learned Models Model Predictive Control AlphaZero and MuZero

Imitation Learning Offline RL Exploration Goal-Conditioned RL Multi-Agent RL

Advanced Topics

Exploration

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

Placeholder content for Exploration.

Offline RL

Learning from fixed datasets without environment interaction.

Goal-Conditioned RL

Hindsight Experience Replay and learning to reach arbitrary goals.