giakoumidis / Collaboration-Competition-Project

Solving Udacity's Deep Reinforcemnt Learning Nano Degree project: Collaboration - Competition

Deep Reinforcement Learning : Collaboration - Competition

This project repository contains my work for the Udacity's Deep Reinforcement Learning Nanodegree Project 3: Collaboration - Competition.

The goal of this project

In this project, the goal is to train two agents control rackets to bounce a ball over a net.

Enviroment

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.

Please note that the project environment is similar to, but not identical to the Tennis environment on the Unity ML-Agents GitHub page.

More detailed description of the original Unity environment can be found in this paper.

Getting Started

Setup the environment

1. Follow steps 1, 3 and 4 in the instructions here to set up your Python environment. By following these instructions, you will install PyTorch, the ML-Agents toolkit, and a few more Python packages that are required for this project.

2. Clone this repository.

3. 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 (64-bit): click here

   • Windows (32-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 (version 1) or this link (version 2) 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.)

4. Place the file in the root folder of this repository, and unzip (or decompress) the file.

5. Run Jupyter Notebook and open the Collaboration-Competition-Project folder.

6. Change the kernel to match the drlnd environment by using the drop-down Kernel menu.

Usage

Follow the instructions in Tennis.ipynb to train the agent.

Please note that the code has been tested in Linux enviroment Ubuntu 18.04 LTS.