Unlocking the Secrets of Actor-Critic Reinforcement Learning: A Beginner’s Guide

Understanding Actor-Critic Mechanisms, Different Flavors of Actor-Critic Algorithms, and a Simple Implementation in PyTorch

Concepts you should Know:

Reinforcement Learning: Temporal Difference Learning

Reinforcement Learning: Q-Learning

Deep Q Learning: A Deep Reinforcement Learning Algorithm

An Intuitive Explanation of Policy Gradient

What is the Actor-Critic algorithm?

Actor-Critic is a Reinforcement Learning algorithm that optimizes the agent’s actions based on the environment's feedback.

The Actor-Critic RL aims to find an optimal policy for the agent in an environment using two components: Actor and Critic.

Actor: The Actor learns an optimal policy by exploring the environment

Critic: The Critic assesses the value of each action taken by the Actor to determine whether the action will result in a better reward, guiding the Actor for the best course of action to take.