Lecture on Reinforcement Learning

The lecture delves into the realm of policy approximation in Reinforcement Learning (RL), specifically focusing on the utilization of neural networks. It explores the concept of policy networks, which learn to map states to actions, enabling agents to make optimal decisions. The discussion also touches upon the integration of actor-critic methods, a powerful technique that combines policy gradient and value function estimation for efficient learning. Additionally, the lecture briefly highlights the potential application of RL in the domain of crop management, showcasing the versatility of these techniques in addressing real-world challenges.