sagarrathod-tomtom / collaboration-competition-drlnd-udacity-project-3

Collaboration and Competition

Project Goal

The goal is to train two agents to learn how to play tennis. Hence it's a multi-agent reinforcement learning task.

Environment Details

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:

1. Clone this Github repository:

git clone https://github.com/SagarRathod-TomTom/Collaboration-Competition-DRLND-Udacity-Project-3.git

2. Setup Python:

Follow this link to setup your environment for traning an agent on your local machine.

3. Download the Unity Environment:

For this project, you will not need to install Unity - this is because we have already built the environment for you, and you can download it 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

Then, place the file in the unity_environment/ folder after you clone this GitHub repository, and unzip (or decompress) the file.

(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.

The second version is useful for algorithms like PPO, A3C, and D4PG that use multiple (non-interacting, parallel) copies of the same agent to distribute the task of gathering experience.

3. Jupyter Notebook:

Executes the code cells in the provided notebook.

Implementation Details:

This repository contains the implementation of DDPG to train multiple agents simulatenously collaborating and competing in the same environment.

Checkout Report.md for in-depth implementation details.