Tennis coach

This repository is an implementation of DDPG agents for the collaboration and competition project of Udacity Deep Reinforcement Learning nanodegree, in the tennis game provided by unity.

Table of Contents

• Environment
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Credits
• License

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 observation space consists of 8 variables corresponding to the position and velocity of the ball and racket. Each agent receives its own, local observation. Two continuous actions are available, corresponding to movement toward (or away from) the net, and jumping.

The task is episodic, and in order to solve the environment, your agents must get an average score of +0.5 (over 100 consecutive episodes, after taking the maximum over both agents). Specifically,

• After each episode, we add up the rewards that each agent received (without discounting), to get a score for each agent. This yields 2 (potentially different) scores. We then take the maximum of these 2 scores.
• This yields a single score for each episode.

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

Getting started

Prerequisites

• Python 3.6 (or more recent)
• pip
• ml-agents v0.4 (check this release if you encounter issues)

Installation

You can install the project requirements as follows:

git clone https://github.com/frgfm/drlnd-p3-collaboration-competition.git
cd drlnd-p3-collaboration-competition
pip install -r requirements.txt

Download the environment build corresponding to your OS

• Linux: here
• Mac OSX: here
• Windows (32-bit): here
• Windows (64-bit): here

Then extract the archive in the project folder.

If you wish to use the agent trained by repo owner, you can download the model parameters as follows:

wget https://github.com/frgfm/drlnd-p3-collaboration-competition/releases/download/v0.1.0/maddpg_actor.pt

Usage

Training

All training arguments can be found using the --help flag:

python train.py --help

Below you can find an example to train your agent:

python train.py --deterministic --no-graphics

Evaluation

You can use an existing model's checkpoint to evaluate your agent as follows:

python evaluate.py --checkpoint ./maddpg_actor.pt

Credits

This implementation is vastly based on the following papers:

• Asynchronous Actor Critic
• Proximal Policy Optimization
• DDPG

License

Distributed under the MIT License. See LICENSE for more information.