Skip to content

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 TopicsOffline RL

Offline RL

Learning from fixed datasets without environment interaction.

To be done soon

Imitation Learning

Behavioral cloning, DAgger, and learning from demonstrations.

Exploration

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