Reinforcement Learning: From Policy Optimization to Multi-Agent Systems

Learn the fundamentals and advanced concepts of reinforcement learning in this 34-minute educational video that delves into reward systems, policy optimization, and transformer architectures. Explore how reward systems guide agent learning by quantifying state-action pair desirability and discover how policies map observed states to possible actions with the goal of maximizing cumulative expected rewards. Master the integration of transformer architectures in reinforcement learning, particularly for handling non-Markovian rewards and complex multi-agent systems. Understand the training process of transformer models in multi-agent environments, including state representation, self-attention mechanisms, and the critical balance between exploration and exploitation. Gain practical insights into Proximal Policy Optimization (PPO) and learn how transformer parameters are adjusted to optimize cumulative rewards. Follow along with code examples and real-world applications in robotics while exploring the intricate relationship between reward functions, policy optimization, and Markov Decision Processes.