Deep RL Course documentation

Generalization in Reinforcement Learning

Deep RL Course

Unit 0. Welcome to the course

Unit 1. Introduction to Deep Reinforcement Learning

Bonus Unit 1. Introduction to Deep Reinforcement Learning with Huggy

Live 1. How the course work, Q&A, and playing with Huggy

Unit 2. Introduction to Q-Learning

Unit 3. Deep Q-Learning with Atari Games

Bonus Unit 2. Automatic Hyperparameter Tuning with Optuna

Unit 4. Policy Gradient with PyTorch

Unit 5. Introduction to Unity ML-Agents

Unit 6. Actor Critic methods with Robotics environments

Unit 7. Introduction to Multi-Agents and AI vs AI

Unit 8. Part 1 Proximal Policy Optimization (PPO)

Unit 8. Part 2 Proximal Policy Optimization (PPO) with Doom

Bonus Unit 3. Advanced Topics in Reinforcement Learning

Introduction Model-Based Reinforcement Learning Offline vs. Online Reinforcement Learning Generalisation Reinforcement Learning Reinforcement Learning from Human Feedback Decision Transformers and Offline RL Language models in RL (Automatic) Curriculum Learning for RL Interesting environments to try An introduction to Unreal Learning Agents An Introduction to Godot RL Students projects Brief introduction to RL documentation

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Generalization in Reinforcement Learning

Generalization plays a pivotal role in the realm of Reinforcement Learning. While RL algorithms demonstrate good performance in controlled environments, the real world presents a unique challenge due to its non-stationary and open-ended nature.

As a result, the development of RL algorithms that stay robust in the face of environmental variations, coupled with the capability to transfer and adapt to uncharted yet analogous tasks and settings, becomes fundamental for real world application of RL.

If you’re interested to dive deeper into this research subject, we recommend exploring the following resource:

Generalization in Reinforcement Learning by Robert Kirk: this comprehensive survey provides an insightful overview of the concept of generalization in RL, making it an excellent starting point for your exploration.
Improving Generalization in Reinforcement Learning using Policy Similarity Embeddings

←Offline vs. Online Reinforcement Learning Reinforcement Learning from Human Feedback→