In this project I trained two agents to cooperate in 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.

1. Install anaconda click here

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

   • Linux or Mac:

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

   • Windows:

   conda create --name drl python=3.6 
   activate drl

3. 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.

4. Clone the repository (if you haven't already!). Then, install several dependencies.

git clone https://github.com/meiermark/rl-navigation.git
cd rl-navigation/ml-agents
pip install .

5. Create an IPython kernel for the drl environment.

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

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

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

2. Place the file in the root directory of this GitHub repository and decompress it.

3. Follow the instructions in Tennis.ipynb to begin the training of the agents!