Deep and Robust Reinforcement Learning Techniques

This lecture covers advanced concepts in reinforcement learning, focusing on deep and robust methods. It begins with a recap of value-based and policy-based approaches, highlighting the actor-critic framework that combines both strategies. The instructor explains the optimization perspective of actor-critic methods, detailing how they utilize policy gradients and temporal difference learning to improve performance. The discussion then shifts to deep reinforcement learning, emphasizing the necessity of neural networks for handling complex environments. The lecture addresses challenges such as sample inefficiency and high variance in training, introducing techniques like experience replay and target networks to stabilize learning. The instructor also explores robust adversarial reinforcement learning, where agents learn to perform well under varying environmental conditions by modeling adversarial interactions. The session concludes with practical insights into implementing these methods effectively, encouraging students to apply these concepts in their projects.