This project contains an agent based on Deep Reinforcement Learning that can learn from zero (no labeled data) to play Tennis. \o/
It's use the Unity Machine Learning Agents Toolkit to design, train, and evaluate deep reinforcement learning algorithms implementations.
The environment used for this project is the Udacity version of the Tennis Environment, from Unity.
We have two versions for this Unity environment:
This project is part of my Deep Reinforcement Learning Nanodegree.
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.
The installation process is divided in three parts:
- Python 3.6.x
- Dependencies
- Unity's Environment
This notebook was developed on AWS SageMaker.
The kernel used is conda_python3
To install and run this project you will need to have Python 3.6.x.
You could download olds Anaconda versions here
The reason for it is that Unity ml-agents needs TensorFlow in version 1.15 and TensorFlow 1.15 needs Python. 3.6.x.
The best way to run this project is on AWS SageMaker.
All packages and their versions are describe in requirements.txt.
You need to run these commands before to install all dependencies:
pip install --upgrade pip
pip install -r requirements.txt
pip -q install ./python
You'll have these three commands in the first part of [Continuous_Control.ipynb], as well.
As you know, we have two different environment versions for this project. Select what you want.
The environment for Linux and Windows (64-bit) is already in that repository.
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 (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.)
Then, place the unzipped folder on the root of this repository.
The contents of this repository are covered under the MIT License.