Skip to content
Implemented DDPG to solve Unity's tennis environment
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Tennis.app/Contents
README.md
Report.pdf
checkpoint_actor.pth
checkpoint_critic.pth
ddpg_agent.py
model.py
project3.ipynb

README.md

Project 3

This project was done as part of the Udacity Deep Reinforcement Nanodegree. Some of the text in this README is adapted from the original Udacity Deep Reinforcement Learning Nanodegree repo. Read Report.pdf for more information.

Introduction

The environment includes two agents that control rackets that play tennis with each other. An agent receives a reward of 0.1 if it hits the ball over the net. It receives a reward of -0.01 if the ball hits the ground or is out of bounds. The goal is to keep the ball in play.

The observation space for each agent is 8 continuous variables representing the position and velocity of the ball and racket. There are 2 continuous action variables --- moving towards or away from the next and jumping.

The solved criteria is an average score of +0.5 over the last 100 consecutive episodes after taking the maximum over both agents.

Getting Started

  1. Make sure you have the following python library dependencies:

    • pytorch
    • numpy
    • unityagents
    • matplotlib
    • jupyter notebook
  2. Clone this repository with the command git clone https://github.com/cjm715/Udacity-drln-p3.git or simply download as a zip folder by using the green button labeled 'clone or download' on this page. Note that there is no need to separately download the Unity environment. The necessary files are included in the repo.

Instructions

Run jupyter notebook and open project3.ipynb.

You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.