Source code for momba.gym

# -*- coding:utf-8 -*-
# Copyright (C) 2019-2021, Saarland University
# Copyright (C) 2019-2021, Maximilian Köhl <>

A formal methods based toolbox for reinforcement learning.

from __future__ import annotations

import typing as t

from .. import model, engine

from . import abstract, env, checker, generic

[docs]def create_generic_env( network: model.Network, controlled_instance: model.Instance, property_name: str, *, parameters: engine.Parameters = None, rewards: generic.Rewards = generic.DEFAULT_REWARD_STRUCTURE, actions: generic.Actions = generic.Actions.EDGE_BY_INDEX, observations: generic.Observations = generic.Observations.GLOBAL_ONLY, renderer: t.Optional[env.Renderer] = None, ) -> env.MombaEnv: """ Constructs a generic training environment from a JANI model based on the provided options. Arguments: network: A JANI automaton network. controlled_instance: An instance of an automaton in the provided network. The decision-making agent trained on the resulting environment is assumed to act by resolving the non-determinism in this automaton. property_name: The name of a reach-avoid JANI property (specified as part of the JANI model the network originates from) for which the agent should be trained. parameters: Allows defining values for parameters of the JANI model. rewards: Specifies the reward structure used for training. actions: Specifies the action space for the environment. observations: Specifies the observation space for the environment. renderer: Is an optional renderer for the OpenAI Gym API. """ return env.MombaEnv( generic.GenericExplorer.create( engine.Explorer.new_discrete_time(network, parameters=parameters), controlled_instance, property_name, rewards=rewards, actions=actions, observations=observations, ), renderer=renderer, )
__all__ = ["abstract", "env", "checker", "create_generic_env"]