Offline RL: From Theory to Industrial Practice
Applied learning notes on offline deep reinforcement learning — not an authoritative textbook. The material summarizes algorithms, implementation patterns, and practical caveats; linked code is educational scaffolding.
A practical guide to offline reinforcement learning — for practitioners who know ML and want to apply RL to real-world systems without live experiments.
Each chapter: idea → formalization → code → limitations.
Contents
- Ch 1Behavioral Cloning ready
- Ch 2The Offline RL Problem: Extrapolation Error ready
- Ch 3Off-Policy Evaluation (OPE) ready
- Ch 4Conservative Q-Learning (CQL) ready
- Ch 5Implicit Q-Learning (IQL) ready
- Ch 6Policy-Constraint and Actor-Critic (TD3+BC, AWAC) ready
- Ch 7Decision Transformers ready
- Ch 8Model-Based Offline RL (MOPO, MOReL) ready
- Ch 9Physics-Informed Offline RL ready
- Ch 10Industrial Applications: Digital Twin & Process Control ready
- Ch 11Explainability in Offline RL ready
- Ch 12Offline RL for Tool-Using LLM Agents ready
- Ch 13Conclusion and Future Directions ready
- App.Algorithm Selection Guide ready