从零开始编写chatGPT | 第二讲: PPO实现

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从零开始编写chatGPT | 第二讲: PPO实现

Table of Contents

  1. Introduction
  2. Implementation Overview
  3. The Goal and Setup
  4. How PPO Works
  5. Phases of PPO
  6. The Card Pole Problem
  7. Model Architecture
  8. Rollout Creation
  9. Generalized AdVantage Estimation
  10. Loss Functions
  11. Training Loop
  12. Breakout Game Implementation

Introduction

In this article, we will dive deep into the implementation details of Proximal Policy Optimization (PPO) in reinforcement learning. PPO is a strong method used not only in reinforcement learning but also in language model fine-tuning through reinforcement learning with human feedback (RLHF). We will explore the various components of PPO, understand its inner workings, and analyze its implementation details. We will also Apply this implementation to a specific problem - the card pole problem - to see how PPO can be used to solve complex tasks.

Implementation Overview

Before delving into the implementation details, let's provide a brief overview. The goal of PPO is to train a neural network policy that, given the Current state of the environment, can take a sequence of actions that maximizes the expected reward. The implementation can be divided into two phases: Phase One involves collecting information by playing the game and creating rollouts, while Phase Two focuses on analyzing and updating the model Based on the rollouts. In the implementation, we will be using a Parallel environment to run multiple trajectories simultaneously, allowing for more efficient training.

The Goal and Setup

In reinforcement learning, the goal of training a neural network is to come up with a policy that, when presented with the state of the system, can take a sequence of actions that maximize the expected reward. For example, in the card pole problem, the neural network agent aims to balance a pole for as long as possible by changing the position of a card. The longer the pole is balanced, the higher the reward obtained. The neural network policy is trained to learn which actions to take to achieve this goal.

How PPO Works

Proximal Policy Optimization (PPO) works by optimizing the policy of the neural network through maximizing a certain loss function. The main loss term in PPO is the clipped pessimistic loss (also known as the surrogate loss), which encourages the policy to take better actions. The loss function also includes terms such as value function loss and entropy loss, which contribute to overall policy improvement and exploration.

Phases of PPO

PPO consists of two phases: Phase One involves collecting rollouts, while Phase Two focuses on analyzing and updating the model based on the collected information. In Phase One, we collect data by playing the game and creating rollouts, which contain information such as logits, values, and advantages. In Phase Two, we analyze the rollouts to calculate losses, update the model using backpropagation, and optimize the policy using the Adam optimizer.

The Card Pole Problem

To illustrate the implementation of PPO, we will use the card pole problem. In this problem, the agent's task is to balance a pole on a card. The action space consists of pushing the card to the left or the right, and the goal is to learn a policy that can balance the pole for as long as possible. This problem serves as a simple and accessible example to demonstrate the implementation of PPO.

Model Architecture

The neural network used in the implementation consists of two heads: one for the value function and one for the policy. The model takes the observation of the environment as input and outputs the value, action probabilities, and entropy. The shared layers between the two heads allow for multitask learning, wherein the model learns to estimate the value function for a given state and choose appropriate actions.

Rollout Creation

Rollouts are an essential component of PPO training. In this phase, we play the game and Collect information such as logits, values, and advantages. The rollouts are stored in a data set, allowing us to Create random mini-batches for training. The rollout creation process is parallelized to improve efficiency.

Generalized Advantage Estimation

Advantage estimation is crucial for PPO training, as it estimates the expected value of taking a specific action compared to other possible actions. Generalized Advantage Estimation (GAE) provides a way to approximate these advantages by recursively calculating advantages based on rewards and values. The implementation of GAE involves calculating advantages using a reverse recursive formula and applying a mask to handle terminated trajectories.

Loss Functions

PPO uses several loss functions to optimize the policy. The main loss term is the clipped pessimistic loss, which is responsible for improving the quality of actions taken by maximizing the policy. Other loss terms, such as value function loss and entropy loss, contribute to better approximation of value function estimates and exploration versus exploitation trade-offs.

Training Loop

The training loop consists of two phases: data collection (Phase One) and model analysis and update (Phase Two). In Phase One, we play the game, create rollouts, and calculate advantages and returns. These rollouts are then stored in a data set for later use. In Phase Two, we analyze the stored rollouts, calculate losses, and update the model using backpropagation and the Adam optimizer. The training loop iterates over multiple epochs, gradually improving the policy over time.

Breakout Game Implementation

In addition to the card pole problem, we provide an example of how to apply the PPO implementation to a more complex game - Breakout. The Breakout game requires a convolutional layer in the neural network due to its image-based nature. Some modifications need to be made to the environment and model settings to adapt them to the Breakout game. This serves as an exercise for readers to further explore and implement PPO for different games and RL problems.

Conclusion

Proximal Policy Optimization is a powerful method in reinforcement learning and language model fine-tuning. Understanding the implementation details of PPO allows us to train more effective models for various tasks. By exploring the different phases, model architectures, and loss functions, we can apply PPO to solve complex problems such as the card pole problem and Breakout game. The implementation provides a foundation for further exploration and experimentation in the field of reinforcement learning.

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