Everything You Need To Master Actor Critic Methods | Tensorflow 2 Tutorial

TL;DR

This tutorial provides a comprehensive overview and coding demonstration of actor critic methods in TensorFlow 2, covering the fundamentals of reinforcement learning and the Markov decision process.

Transcript

welcome back everybody in today's tutorial you are going to get a mini crash course in actor critic methods in tensorflow 2. we're going to have around 15 minutes of lecture followed by about 20 minutes of coding and you're going to learn everything you need to know to go from start to finish with actor critic methods if you'd like to know more you... Read More

Key Insights

• 🧑‍🏭 Reinforcement learning involves agents acting on an environment, receiving rewards, and trying to maximize their total reward over time.
• 😒 The Markov property allows for the use of probabilities and expectation values in reinforcement learning.
• ⚾ The policy determines how the agent chooses actions based on the current state.
• 🚂 Value and action value functions are estimated using neural networks, which can be trained using temporal difference learning.
• 🧑‍🏭 Actor critic methods combine the estimation of the value function and the policy to learn how to act optimally in the environment.
• ❓ Discounting is used to prioritize immediate rewards and account for uncertainty in future states.
• 🧑‍🏭 Actor critic methods can be implemented in TensorFlow 2 using deep neural networks, with the actor network approximating the policy and the critic network approximating the value function.

Questions & Answers

Q: What is reinforcement learning?

Reinforcement learning is a type of machine learning where an agent learns to take actions in an environment to maximize its total reward over time.

Q: What is the Markov property?

The Markov property refers to a system that only depends on its previous state and the last action taken by the agent. It allows for the use of certain mathematical techniques in reinforcement learning.

Q: What is the policy in reinforcement learning?

The policy is a mathematical function that takes states as inputs and returns probabilities of selecting each action. It determines how the agent chooses actions based on the current state.

Q: How are value and action value functions estimated?

Value and action value functions are estimated using neural networks. The neural network approximates the value of states or state-action pairs based on the agent's experience in the environment.

Q: What is the role of the critic in actor critic methods?

The critic approximates the value function and provides feedback to the actor. It evaluates the states/actions taken by the actor and guides its decision-making process.

Q: How are actor critic methods trained?

Actor critic methods are trained using temporal difference learning. The weights of the neural networks are updated at each time step based on the observed rewards and the estimated values.

Q: What is the role of discounting in the reinforcement learning problem?

Discounting is used to reduce the contribution of future rewards in the total returns. It is a way to prioritize immediate rewards and account for uncertainty in future states.

Q: How can actor critic methods be implemented in TensorFlow 2?

Actor critic methods can be implemented in TensorFlow 2 using deep neural networks. The actor network approximates the policy, while the critic network approximates the value function.

Summary & Key Takeaways

• This tutorial provides a crash course in actor critic methods in TensorFlow 2, with a focus on reinforcement learning and the Markov decision process.

• It covers the components of a Markov decision process, including states, actions, rewards, and policies.

• The tutorial explains the concept of the value function and the action value function, and how they are estimated using neural networks.