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

Actor-Critic & Continuous Control

Actor-Critic Framework

A2C, advantage estimation, and Generalized Advantage Estimation (GAE).

Placeholder content for Actor-Critic Framework.

RL for Sequence Generation and RLHF

Applying policy gradient to sequence models, reward modeling, and alignment.

DDPG

Deep Deterministic Policy Gradient for continuous action spaces.