Project 3 : Collaboration and Competition

Project Details:

For this project, two agents are trained to 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:

To run the code, you need to have PC Windows (64-bit) with Anaconda with Python 3.6 installed.

To download Anaconda, please click the link below:

https://www.anaconda.com/download/

Clone or download and unzip the drnld-p3-mad4pg folder.

Download by clicking the link below and unzip the environment file under drnld-p3-mad4pg folder

https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Tennis/Tennis_Linux_x86_64.zip

Download by clicking the link below and unzip the ml-agents file under drnld-p3-mad4pg folder

https://github.com/Unity-Technologies/ml-agents/tree/0.4.0b

Dependencies :

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

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

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

git clone https://github.com/udacity/deep-reinforcement-learning.git
cd deep-reinforcement-learning/python
pip install .

Create an IPython kernel for the drlnd environment.

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

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

Run the code

Open Tennis.ipynb in Jupyter and press Ctrl+Enter to run the first cell to import all the libraries.

Train an agent

Run the cells which contain the "train" function

sunilnandihalli / drnld-p3-mad4pg