Lecture on Actor-Critic agents for Reinforcement Learning

The lecture provides an overview of policy approximation in Reinforcement Learning (RL), with a particular emphasis on the use of neural networks. It introduces the concept of policy networks, which learn to map states to optimal actions. The lecture further explores the integration of actor-critic methods, a powerful technique that combines policy gradient and value function estimation. To illustrate the practical applications of RL, the lecture briefly discusses its potential in the field of crop management.