Multi-Agent RL in Tennis Virtual Game

Introduction

For this project, you will work with the Tennis 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

Follow the instructions below to explore the environment on your own machine! You will also learn how to use the Python API to control your agent.

Step 1: Set Up The Depedencies

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

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

   • Linux or Mac:

   conda create --name drlnd python=3.6
   source activate drlnd

   • Windows:

   conda create --name drlnd python=3.6 
   activate drlnd

2. Follow the instructions in this repository to perform a minimal install of OpenAI gym.

   • Next, install the classic control environment group by following the instructions here.
   • Then, install the box2d environment group by following the instructions here.

3. Clone the repository (if you haven't already!), and navigate to the python/ folder. Then, install several dependencies.

git clone https://github.com/jhonsonlee/multi-agent-in-tennis-virtual-game.git
cd multi-agent-in-tennis-virtual-game/python
pip install .

4. Create an IPython kernel for the drlnd environment.

python -m ipykernel install --user --name drlnd --display-name "drlnd"

5. Before running code in a notebook, change the kernel to match the drlnd environment by using the drop-down Kernel menu.

(For Windows users) The ML-Agents toolkit supports Windows 10. While it might be possible to run the ML-Agents toolkit using other versions of Windows, it has not been tested on other versions. Furthermore, the ML-Agents toolkit has not been tested on a Windows VM such as Bootcamp or Parallels.

Step 2: Download the Unity 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.

   (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 the multi-agent-in-tennis-virtual-game repository.

Instructions

Navigating The Source Code

There are three important files which contains the source code to run and train our agent:

• Tennis.ipynb contains the instructions to explore the environment, train the agent to be smart and run the smart agent.
• /src/ddpg_agent.py contains the source code which describes how the agent works using DDPG Algoritm.
• /src/model.py contains the source code of deep learning model for the DDPG algoritm.

Explore the Environment

In order to understand how to create a smart agent, we must recognize the environment first. Please, open Tennis.ipynb using Jupyter Notebook and follow these steps:

• Step 1 : Start the Environment
• Step 2 : Examine the State and Action Spaces
• Step 3 : Take Random Actions in the Environment

Train the Agent

As you can see in the Explore the Environment section, the agent is still taking random actions and generate zero points during the gameplay. In order to create a smart agent, we need to train the agent by following these two steps:

• Step 4 : Contruct the Train Method
• Step 5 : Train the Agent

Watch the Smart Agent Play

After we train to agent how to better play the game, we can watch the agent play with better decision and better score. Howevever, we can not see the agent play in live gameplay in this notebook. But, we can see the agent correctly play the game by seeing the movement of the score on the terminal. Please follow the Step 6 : Watch the Agent Play.

(Optional) Challenge: Crawler Environment

After you have successfully completed the project, you might like to solve the more difficult Soccer environment.

In this environment, the goal is to train a team of agents to play soccer.

You can read more about this environment in the ML-Agents GitHub here. To solve this harder task, you'll need to download a new Unity environment. (Note: Udacity students should not submit a project with this new environment.)

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 p3_collab-compet/ folder in the DRLND GitHub repository, and unzip (or decompress) the file. Next, open Soccer.ipynb and follow the instructions to learn how to use the Python API to control the agent.

(For AWS) If you'd like to train the agents 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 agents without enabling a virtual screen, but you will be able to train the agents. (To watch the agents, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)

License

This repository is under the MIT license.