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Reinforcement Learning for Atari Games

Purpose

This project aims to implement and compare three Reinforcement Learning (RL) algorithms, Deep Q Networks (DQN), Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO), using PyTorch. The goal is to train agents to beat Atari games such as Pong, demonstrating the effectiveness of these algorithms in learning complex tasks.

Table of Contents

Skills Used

  • Reinforcement Learning Algorithms (DQN, A2C, PPO)
  • Deep Learning (PyTorch Framework)
  • Python Programming
  • OpenAI Gym Environment

Technologies Used

  • Python
  • PyTorch
  • OpenAI Gym (for Atari game environments)

Project

The project achieved the following results:

  • Trained Deep Q Networks (DQN) agent to achieve high scores in Atari games, including Pong.
  • Implemented prioritized experience replay in DQN for efficient sampling.
  • Developed an Advantage Actor-Critic (A2C) agent that combines value-based and policy-based learning methods.
  • Developed a Proximal Policy Optimization (PPO) agent, known for its stable and reliable policy gradient updates.
  • Evaluated and compared the performance of DQN, A2C, and PPO on Atari games.

Background

Deep Q Networks (DQN)

DQN is a value-based RL algorithm that learns to approximate the Q-value function. It uses a neural network to estimate the Q-values for each action in a given state. In this project, DQN is enhanced with a prioritized memory buffer, which improves learning efficiency by prioritizing experience replay based on temporal difference error.

Advantage Actor-Critic (A2C)

A2C is a policy-based RL algorithm that combines actor and critic networks. The actor network learns a policy to select actions, while the critic network evaluates the state-value function. By leveraging both value-based and policy-based learning, A2C aims to improve sample efficiency and stability in training.

Proximal Policy Optimization (PPO)

PPO is a policy-based RL algorithm that improves the stability and reliability of policy updates by limiting the size of policy changes. It does this by using a clipped objective function to ensure that updates do not move the policy too far from the previous policy. This makes PPO particularly effective for complex environments like Atari games.