SlimeVolleyGym is a simple gym environment for testing single and multi-agent reinforcement learning algorithms.
The game is very simple: the agent's goal is to get the ball to land on the ground of its opponent's side, causing its opponent to lose a life. Each agent starts off with five lives. The episode ends when either agent loses all five lives, or after 3000 timesteps has passed. An agent receives a reward of +1 when its opponent loses or -1 when it loses a life.
This environment is based on , a JavaScript game I created in that used self-play and evolution to train a simple neural network agent to play the game better than most human players. I decided to port it over to Python as a lightweight and fast gym environment as a testbed for more advanced RL methods such as multi-agent, self-play, continual learning, and imitation learning algorithms.
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