Master Proximal Policy Optimization with OpenAI Gym

Master Proximal Policy Optimization with OpenAI Gym

Table of Contents:

1. Introduction
2. Installation Guide
3. Understanding the Proximal Policy Optimization Algorithm
4. Implementing the Actor Model
5. Implementing the Critic Model
6. Generalized AdVantage Estimation
7. Training the PPO Agent
8. Testing the PPO Agent
9. Multi-Process Training
10. Conclusion

Introduction

Welcome to another part of my step-by-step reinforcement learning tutorial with Jim and TensorFlow 2. In this tutorial, I will Show You how to implement a reinforcement learning algorithm known as Proximal Policy Optimization (PPO). We will use PPO to teach an AI agent how to land a rocket in a game called Lunar Lander B2. By the end of this tutorial, you will have a good understanding of how to Apply on-policy learning methods in a real game environment.

Installation Guide

Before we start, let's go through the installation process for the game environment and required libraries. The game environment we will be using is the OpenAI Gym. To install Gym, you can use the following commands:

pip install gym
pip install box2d

Please note that the installation process may vary depending on your operating system. You can find detailed instructions on the Gym GitHub page.

Next, you will need to clone my GitHub repository which contains the code for this tutorial. Make sure you have the necessary system dependencies and Python packages installed by running:

pip install -r requirements.txt

Once everything is set up, we can move on to understanding the Proximal Policy Optimization algorithm.

Understanding the Proximal Policy Optimization Algorithm

Proximal Policy Optimization (PPO) is an on-policy reinforcement learning algorithm introduced by the OpenAI team in 2017. It quickly became one of the most popular reinforcement learning methods due to its stability and excellent performance.

PPO works by collecting a small batch of experiences from the environment and using that batch to update its decision-making policy. The main idea behind PPO is to make small updates to the policy so that the new policy is not too far from the old policy. This is achieved using a technique called clipping, which limits the magnitude of policy updates.

To implement PPO, we will need two models: the actor and the critic. The actor model learns the policy, determining which action to take Based on the observed state of the environment. The critic model evaluates the value of the action taken by the actor. Both models will be trained using the Proximal Policy Optimization loss function.

In the next few sections, we will dive deeper into implementing each component of the PPO algorithm.

Implementing the Actor Model

The actor model is responsible for learning the policy. It takes the Current state of the environment as input and outputs a probability distribution over the possible actions. We will implement the actor model using a deep neural network.

To Create the actor model, we need to define the input Shape, output size (number of actions), and network architecture. In our case, we will use a three-layer deep neural network. The final layer will have a softmax activation function to output the action probabilities.

Next, we will compile the actor model using the Adam optimizer and a custom PPO loss function. The PPO loss function calculates the ratio between the newly updated policy and the old policy to determine the policy update size. Clipping is applied to ensure the policy update is within a certain range.

Implementing the Critic Model

The critic model is responsible for evaluating the value of actions taken by the actor. It takes the current state as input and outputs a single value, indicating the quality of the action.

The structure of the critic model is similar to the actor model, but instead of a softmax activation function, it uses none or tanh. We will also use the PPO loss function for the critic model, applying clipping to limit policy updates.

Generalized Advantage Estimation

In reinforcement learning, it is important to estimate the advantage of taking a particular action in a given state. The advantage can be defined as the measure of improvement obtained by taking an action.

To calculate the advantage, we will use a technique called Generalized Advantage Estimation (GAE). GAE takes into account future rewards and discounts them to give more weight to immediate rewards. This allows the agent to evaluate the advantage of an action over both the short and long term.

During training, we will Collect rewards and use GAE to calculate the advantage. This advantage will be used to update the actor and critic models.

Training the PPO Agent

Now that we have the actor and critic models defined, as well as the GAE advantage estimation, we can start training our PPO agent.

The training process involves interacting with the game environment for a fixed number of steps and collecting experiences such as states, actions, rewards, and advantages. These experiences will be used to update the models and improve the agent's policy.

We will use the fit function of Keras to train both the actor and critic models. The training process will involve running through a loop for a certain number of episodes, collecting experiences, and updating the models.

Testing the PPO Agent

Once the agent is trained, we can test its performance in the game environment. We will load the saved actor and critic models and use them to Interact with the environment.

During testing, you will see the model taking different actions and collecting rewards from the environment. If everything works correctly, you should observe the agent making progress and improving its performance over time.

Multi-Process Training

To speed up the training process, we can utilize multi-processing. By running multiple game environments in Parallel, we can collect experiences more efficiently and train the agent faster. We will explore this technique in Detail in a separate tutorial.

Conclusion

In this tutorial, we implemented the Proximal Policy Optimization algorithm for training an AI agent to land a rocket in the Lunar Lander B2 game. We discussed the installation process, explained the different components of the PPO algorithm, and demonstrated how to train and test the PPO agent. By following this tutorial, you should have a good understanding of PPO and how to apply it to reinforcement learning problems.