Standard RL
If you want to use Mighty on standard RL environments, you can choose between the Gymnasium interface and Pufferlib as a fast alternative. Generally we recommend you use Pufferlib where possible, but the choice is yours!
Mighty on Gymnasium Environments#
Mighty can be used as a standard RL library for all environments that follow the Gymnasium interface. In order to run a Mighty Agent, use the run_mighty.py script and provide any training options as keywords. If you want to know more about the configuration options, call:
An example for running the PPO agent on the Pendulum gym environment for 1000 steps looks like this:
We assume that if you don't specify anything beyond the name, you want to use Gymnasium. This will also work for environments that are registered with Gymnasium upon installation, e.g. Gymnasium Robotics and others. You can assume specifying the environment name like this to work just like "gym.make()".
Mighty on Pufferlib Environments#
Pufferlib offers an efficient way to parallelize environment evaluations for a wide selection of tasks. Many well-known Gymnasium environments or benchmarks like ProcGen are included in Pufferlib and we recommend it as a default for these. Running Pufferlib environments is very similar to running Gymnasium environments, you only need to add the pufferlib domain:
python mighty/run_mighty.py 'num_steps=1000' 'algorithm=ppo' 'env=pufferlib.environments.procgen.bigfish'
We have some example configs where the env domain is pre-configured and you can override the name only. An example for minigrid would be:
env_name: MiniGrid-DoorKey-8x8-v0 # Overide with names z.B MiniGrid-LavaGapS5-v0, MiniGrid-DoorKey-8x8-v0, MiniGrid-ObstructedMaze-1Dl-v0, MiniGrid-KeyCorridorS3R2-v0, MiniGrid-UnlockPickup-v0
env: pufferlib.environments.minigrid.${env_name}
env_kwargs: {}
Meaning you can use this configuration (let's call it pufferlib_minigrid) with just the env name, similar to above: