Collaboration and Competition using MADDPG

Introduction

For this project, I will work with the Tennis environment.

This project uses Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to train two agents to control rackets to bounce a ball over a net.

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

Pre-requisites

• Make sure you having a working version of Anaconda on your system.

Step 1: Create and activate Conda environment

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

- __Linux__ or __Mac__: 
```bash
conda create --name drlnd python=3.6
source activate drlnd
```
- __Windows__: 
```bash
conda create --name drlnd python=3.6 
activate drlnd
```

Step 2: Clone the repo

Clone this repo using https://github.com/sriramjaju/collaboration-and-competition-using-maddpg.git. Navigate to the python folder. Then, install several dependencies.

Step 3: Download Tennis environment

You will also need to install the pre-built Unity environment, you will NOT need to install Unity itself. Select the appropriate file for your operating system:

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.

   (For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)

2. Place the file in this repository, and unzip (or decompress) the file.

Instructions

Follow the instructions in Tennis.ipynb to get started with training your own agent!
Feel free to experiment with modifying the hyperparameters to see how it affects training.

Report

See the report for more details on the implementation.