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
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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.
The Actor then uses the feedback from Critic to adjust its policy and make more informed decisions, leading to improved overall performance.
The Actor-Critic is a combination of value-based, and policy-based methods where the Actor controls how our agent behaves using the Policy gradient, and the Critic evaluates how good the action taken by the Agent based on value-function.
In value-based methods, the value function is estimated to predict the expected future reward for a given state or action.
Policy-based methods directly map states to actions through a policy. The policy is updated using the policy gradient theorem, which updates the policy in the gradient direction to increase the expected reward.
