The environment is composed of two agents each one controlling a racket 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.
Therefore, the overall episode reward is maximized if the ball is kept in play by both agents
Each timestep, each agent recieves:
- A 24 element long vector representing the current state
The actions space is continuous and consists of the movement toward or away from the net, and jumping.
Finally, the environment is considered solved when the average score of the last 100 episodes is greater than +0.5.
The episode score is calculated by adding up the rewards that each agent received (without discounting) then taking the maximum of these two scores.
To train the agent simply run python train_tennis_ddpg.py. All hyperparameters can be modified within the script file.
A trained model with an average score of 1.26 over 100 episodes is included in this repository.
For a more complete description of the results, refer to the report page.
To visualise the trained agent either follow this link or run:
python watch_trained_tennis.py --agent data/tennis_ddpg.ckpt
Create a new Python 3.6 environment.
conda create --name tennis python=3.6
activate tennis
Install ml-agents using the repository.
git clone https://github.com/Unity-Technologies/ml-agents.git
cd ml-agents
git checkout 0.4.0b
cd python
pip install .
Install PyTorch using the recommended pip command from the PyTorch site. For example, to install with CUDA 9.2:
conda install pytorch cuda92 -c pytorch
Clone this repository locally.
git clone https://github.com/ostamand/tennis.git
Finally, download the environment which corresponds to your operationg system. Copy/paste the extracted content to the data subfolder.