maddpg-tennis

A Multi Agent Deep Deterministic Actor-Critic reinforcement learning solution in python for the Unity ML (Udacity) Tennis environment

Introduction

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.

Getting Started

To set up your python environment and run the code in this repository, follow the instructions below.

setup Conda Python environment

Create (and activate) a new environment with Python 3.6.

• Linux or Mac:

	conda create --name ddpg-rl python=3.6
	source activate ddpg-rl

• Windows:

	conda create --name ddpg-rl python=3.6 
	activate ddpg-rl

Download repository

Clone the repository and install dependencies

	git clone https://github.com/kotsonis/maddpg-tennis.git
	cd maddpg-tennis
	pip install -r requirements.txt

Install Tennis environment

1. Download the environment from one of the links below. You need only select the environment that matches your operating system:

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

   (For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.

2. Place the file in the maddpg-reacher folder, and unzip (or decompress) the file.

3. edit tennis_env.cfg to set the env entry to point to the right location. Example :

--env=./Tennis_Windows_x86_64/Tennis.exe

Commandline arguments

maddpg-reacher uses the Abseil library for logging and argument parsing You can get the CLI options by running

python tennis.py -h

Training

To train the agents, tennis.py reads the hyperparameters from training.cfg and accepts command line options to modify parameters and/or set saving options.You can train the agents with standard parameters as follows

python tennis.py --flagfile=training.cfg

Playing with a trained model

you can see the agents playing with a pre-trained model as follows:

python tennis.py --flagfile=play.cfg

You can also specify the number of episodes you want the agent to play, as well as the non-default trained model as follows:

python tennis.py --play --episodes 20 --load v2_model.pt

Implementation and results

You can read about the implementation details and the results obtained in Report.md