3rd Party Support
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MLPro allows you to reuse widely-used packages and integrate them to MLPro interface by calling wrapper classes.
At the moment, a wrapper class for OpenAI Gym Environments has been tested and is ready-to-use. However, it has not been very stable yet and some minor improvements might be needed.
In the near future, we are going to add wrapper classes for PettingZoo and Ray RLlib.
Soure code of available wrappers: https://github.com/fhswf/MLPro/blob/main/src/mlpro/rl/wrappers.py
OpenAI Gym Environments
Our wrapper class for gym environment is pretty straightforward. You can just simply apply a command to setup a gym-based environment, while creating a scenario.
from mlpro.rl.wrappers import WrEnvGym
self._env = WrEnvGym([gym environment object], p_state_space:MSpace=None, p_action_space:MSpace=None, p_logging=True)
For more information, please check our how to files here.
PettingZoo Environments
Under construction. The wrapper will be available soon.
from mlpro.rl.wrappers import WrEnvPZoo
self._env = WrEnvPZoo([zoo environment object], p_state_space:MSpace=None, p_action_space:MSpace=None, p_logging=True)
Ray RLlib
Under construction. The wrapper will be available soon.
from mlpro.rl.wrappers import wrPolicyRay
wrPolicyRay(...)