AgarCL

A research platform for continual RL that allows for a progression of increasingly sophisticated behaviour.

AgarCL is based on the game Agar.io. It’s a non-episodic, high-dimensional problem featuring stochastic, ever-evolving dynamics, continuous actions, and partial observability:

import gymnasium as gym

# Initialise the environment
env = gym.make("agario-grid-v0", render_mode="human")

# Reset the environment to generate the first observation
observation = env.reset()
for _ in range(1000):
    # this is where you would insert your policy
    action_space = gym.spaces.Box(low=-1, high=1, shape=(2,))
    action = (action_space.sample(), np.random.randint(0, 3))

    # step (transition) through the environment with the action
    # receiving the next observation, reward and if the episode has terminated or truncated
    observation, reward, terminated, truncated, info = env.step(action)
    
    # Update the on-screen display
    env.render()

    # If the episode has ended then we can reset to start a new episode
    if terminated or truncated:
        observation = env.reset()

env.close()

The GitHub repository for the platform is available here, and the repository for the agents can be found here.