This project is a simulated air hockey gaming environment.
Either one can use this to play for fun, capture state, or train reinforcement learning models.
Currently, this project supports 6 types of reinforcement learning techniques: tabular Q-learning, Double DQN (DDQN), c51-DDQN, Dueling DDQN, Synchronous Actor/Critic (A2C), and Proximal Policy Optimization (PPO).
This project is Python 3.6+ compatible and uses Pipenv for depencency management.
To install pipenv, run: pip install pipenv.
To install all depencencies for this project, run pipenv install.
To enter into the virtual environment created by pipenv, run pipenv shell.
This project uses Redis. Either pull down a Docker image or download Redis locally.
For reference, the left mallet is the robot, and the right mallet is the opponent.
We can either train our agents with the game.py script. There a few cli flags we use. You have the --model flag which specify which reinforcement strategy we want to use. If we set --human, then the training script will look to Redis for the human player's input instead of tracking it's position in the air hockey environment instance else it trains against a heuristic-based approach. To have a human opponent, you need to use the web ui found here.
To play the game, use the --play flag and training will not occur.
To view and interact with the agents, you can run the web ui found in this repo.
- Beware of how you set your rewards because these settings drastically effect the exploitation/exploration tradeoff.
Reinforment learning information: