How to Implement Policy Gradients in TensorFlow 2

TL;DR

To implement the policy gradient algorithm in TensorFlow 2, start by creating a policy gradient network using Keras. This algorithm allows an agent to learn from interactions in an environment by approximating the action probabilities and maximizing future rewards through a Monte Carlo method, efficiently calculated using TensorFlow's gradient tape.

Transcript

welcome back everybody in today's tutorial you are going to learn how to code the policy gradient algorithm in tensorflow 2. you don't need any prior exposure to reinforcement learning you just have to follow along let's get started before we begin a word from our sponsor this video is of course sponsored by my udemy courses where i show you how to... Read More

Key Insights

• 👻 TensorFlow 2's gradient tape allows for efficient calculation of gradients in the policy gradient algorithm.
• 🚱 The policy gradient algorithm approximates the agent's policy by maximizing the probability of selecting profitable actions while maintaining non-zero probabilities for other actions.
• 🍉 The discounted sum of future rewards helps account for uncertainty and the long-term consequences of the agent's actions.

Summary & Key Takeaways

• This tutorial teaches how to implement the policy gradient algorithm in TensorFlow 2, specifically for the Lunar Lander environment.

• It covers the creation of the policy gradient network using Keras and explains the significance of the network's layers.

• The agent class is implemented to handle actions, rewards, and memory, and a learning function is included to update the agent's policy based on experiences.

• The main loop is coded to play multiple episodes and track scores, with a plot learning function to visualize the agent's progress.