This repository provides a comprehensive Reinforcement Learning theory book, written in Russian, covering the foundations of deep RL algorithms with detailed proofs. It is targeted at researchers and practitioners seeking a rigorous understanding of RL concepts, offering a structured approach from foundational theory to advanced algorithms.

How It Works

The book systematically builds knowledge, starting with meta-heuristics and classic RL theory, including Bellman equations and policy improvement theorems. It then delves into value-based methods (DQN variants), policy gradient methods (REINFORCE, PPO), continuous control algorithms (DDPG, SAC), and model-based approaches (MCTS, AlphaZero). The final chapters explore advanced topics like imitation learning, intrinsic motivation, and multi-agent RL.

Quick Start & Requirements

The primary content is the full book available on Arxiv: https://arxiv.org/abs/2201.09746. No specific software installation is required to read the theoretical content.

Highlighted Details

• Covers a wide spectrum of RL algorithms, from foundational concepts to state-of-the-art techniques.
• Includes detailed proofs for key algorithms, offering a deep theoretical understanding.
• Organized into chapters covering meta-heuristics, classic theory, value-based, policy gradient, continuous control, model-based, and advanced RL topics.
• Provides a structured learning path for those interested in the theoretical underpinnings of deep RL.

Maintenance & Community

This appears to be a static resource, with no active development or community interaction indicated in the README.

Licensing & Compatibility

The licensing is not specified in the README. Compatibility for commercial or closed-source use is unknown.

Limitations & Caveats

The book is written entirely in Russian, which may be a barrier for non-native speakers. The README does not indicate any associated code repositories or practical implementations for the discussed algorithms.