Review
Review deep learning and see why RL needs neural networks — the bridge to DQN and policy gradients.
Review ML Foundations and see why linear models fail on complex patterns — motivation for neural networks.
Review Volume 1 concepts and preview Volume 2. From dynamic programming (model-given) to model-free methods.
Review Volume 2 tabular methods and preview Volume 3. From Q-tables to neural network function approximation.
Review Volume 3 (DQN and variants) and preview Volume 4 (Policy Gradients). From value-based to policy-based methods.
Review Volume 4 (Policy Gradients, Actor-Critic, DDPG, TD3) and preview Volume 5 (PPO, TRPO, SAC — stable, scalable policy optimization).
Review Volume 5 (PPO, TRPO, SAC) and preview Volume 6 (Model-Based RL — learning world models and planning).
Review Volume 6 (Model-Based RL, MCTS, Dyna-Q, world models) and preview Volume 7 (Exploration — intrinsic motivation, curiosity, and sparse rewards).
Review Volume 7 (Exploration, ICM, RND, Go-Explore, Meta-RL) and preview Volume 8 (Offline RL, Imitation Learning, RLHF).
Review Volume 8 (Offline RL, Imitation Learning, IRL, RLHF) and preview Volume 9 (Multi-Agent RL — cooperation, competition, game theory).
Review Volume 9 (Multi-Agent RL, game theory, QMIX, MAPPO) and preview Volume 10 (Real-World RL — safety, alignment, LLMs, deployment).