About this document
Actor-Critic Methods in Reinforcement Learning by test fish is a document available to read on EtoBox.
The document discusses Actor-Critic methods in reinforcement learning, highlighting the roles of the policy network (actor) and value network (critic). It outlines how these networks are trained using neural networks to approximate state-value and action-value functions, using techniques like temporal difference learning and policy gradients. The algorithm is summarized in a series of steps detailing the process of observing states, sampling actions, and updating network parameters based on rewards and valu
- Author
- test fish
- Language
- EN