Reinforcement Learning Theory Book (rus)

Full book on Arxiv: https://arxiv.org/abs/2201.09746

• Ch. 1: Introduction
• Ch. 2: Meta-heuristics
  • NEAT, WANN
  • CEM, OpenAI-ES, CMA-ES
• Ch. 3: Classic theory
  • Bellman equations
  • RPI, policy improv. theorem
  • Value Iteration, Generalized Policy Iteration
  • Temporal Difference, Q-learning, SARSA
  • Eligibility Traces, TD-lambda, Retrace
• Ch. 4: Value-based
  • DQN
  • Double DQN, Dueling DQN, PER, Noisy DQN, Multi-step DQN
  • c51, QR-DQN, IQN, Rainbow DQN
• Ch. 5: Policy Gradient
  • REINFORCE, A2C, GAE
  • TRPO, PPO
• Ch. 6: Continuous Control
  • DDPG, TD3
  • SAC
• Ch. 7: Model-based
  • Bandits
  • MCTS, AlphaZero, MuZero
  • LQR
• Ch. 8: Next Stage
  • Imitation Learning / Inverse Reinforcement Learning
  • Intrinsic Motivation
  • Multi-Task and Hindsight
  • Hierarchical RL
  • Partial observability
  • Multi-Agent RL