Udacity Deep Reinforcement Learning Nanodegree - Project 3: Collaboration and Competition

Introduction

For this Udacity project, I used a single DDPG agent to solve the Tennis multi-agent collaboration environment.

In this environment, two agents control rackets to bounce a ball over a net. If an agent hits the ball over the net, it receives a reward of +0.1. If an agent lets a ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01. Thus, the goal of each agent is to keep the ball in play.

The environment is considered solved, when the average (over 100 episodes) of those scores is at least +0.5.

Getting Started

Codes were written using Python3.6.2, run this to install necessary packages:

pip install -r requirements.txt

The Unity environment Tennis.app only runs on Macs, for Windows users, download the Windows 32-bit version here.

Please follow the instruction in the Udacity DRLND Instructions on setting up the environment.

Instructions

• Run all the block cells Report.ipynb to initiate the environment and go through the trainings.
• Run evaluate.py to watch a trained agent play the game with itself.
• best_actor.pth and best_critic.pth are the weights of the best model's actor and critic networks.
• network.py defines the DDPG agent's neural networks.
• ddpy_agent.py defines the DDPG agent class.

Sources

• Continuous control with deep reinforcement learning
• Udacity Learning Materials