laser-learning-environment 0.1.1

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Laser Learning Environment (LLE)

LLE is a fast Multi-Agent Reinforcement Learning environment written in Rust which has proven to be a difficult exploration benchmark so far.

Overview

The agents start in the start tiles, must collect the gems and finish the game by reaching the exit tiles. There are five actions: North, South, East, West and Stay.

When an agent enters a laser of its own colour, it blocks it. Otherwise, it dies and the game ends.

Citing our work

The paper has been presented at EWRL 2023. https://openreview.net/pdf?id=IPfdjr4rIs

@inproceedings{molinghen2023lle,
  title={Laser Learning Environment: A new environment for coordination-critical multi-agent tasks},
  author={Molinghen, Yannick and Avalos, Raphaël and Van Achter, Mark and Nowé, Ann and Lenaerts, Tom},
  year={2023},
  series={European Workshop on Reinforcement Learning},
  booktitle={EWRL 2023}
}

Usage

Low level control

The World class is the low-level object that you can work with.

from lle import World, Action
world = World("S0 G X") # Linear world with start S0, gem G and exit X
world.reset()
available_actions = world.available_actions[0] # Action.STAY, Action.EAST
reward = world.step([Action.EAST])
reward = world.step([Action.EAST])
assert world.done

You can also access and force the state of the world

state = world.get_state()
...
world.set_state(state)

You can query the world on the tiles with world.start_pos, world.exit_pos, world.gem_pos, ...

High-Level control

You can also use LLE as an RLEnv with the lle.LLE class. This class is a wrapper around the World class that implements the RLEnv interface.

from lle import LLE
env = LLE.from_str("S0 G X")
obs = env.reset()
obs, reward, done, info = env.step([0]) # Actions are now integers