Source code for l2rpn_baselines.PPO_RLLIB.env_rllib

# Copyright (c) 2020-2022 RTE (
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
# you can obtain one at
# SPDX-License-Identifier: MPL-2.0
# This file is part of L2RPN Baselines, L2RPN Baselines a repository to host baselines for l2rpn competitions.

import grid2op
from grid2op.gym_compat import (BoxGymActSpace,
from grid2op.gym_compat.utils import ALL_ATTR_CONT
from grid2op.gym_compat.box_gym_obsspace import ALL_ATTR_OBS
from l2rpn_baselines.PPO_SB3 import remove_non_usable_attr

if GymEnv._gymnasium:
    import gymnasium as gym
    from gymnasium.spaces import Box
    import gym
    from gym.spaces import Box

[docs]class Env_RLLIB(gym.Env): """ This class represents the Environment usable from rllib, mapping a grid2op environment. It is primarily made to serve as example of what is possible to achieve. You might probably want to customize this environment to your specific needs. This agents uses the rllib framework to code for a neural network. .. warning:: A grid2op environment is created when this agent is made. We found out rllib worked better this way. .. alert:: This class inherits either from `gymnasium.Env` or `gym.Env` depending on grid2op underlying `GymEnv` base class. Please consult grid2op docs for more information. By default it should be a gymnasium environment ! To be built, it requires the `env_config` parameters. This parameter is a dictionnary with keys: - "env_name": the name of the environment you want to make - "obs_attr_to_keep": the attributes of the observation you want to use in the gym observation space (gym observation space is converted to a Box) - "act_attr_to_keep" : the attributes of the action you want to use in the gym action space (gym action space is also converted to a Box) - "backend_class": the type of backed to use - "backend_kwargs": the extra key word arguments to used when creating the backend - all other arguments are passed to `grid2op.make(...)` function """ def __init__(self, env_config): # boilerplate code... # retrieve the information if not "env_name" in env_config: raise RuntimeError("The configuration for RLLIB should provide the env name") nm_env = env_config["env_name"] del env_config["env_name"] obs_attr_to_keep = None if "obs_attr_to_keep" in env_config: obs_attr_to_keep = env_config["obs_attr_to_keep"] del env_config["obs_attr_to_keep"] act_attr_to_keep = None if "act_attr_to_keep" in env_config: act_attr_to_keep = env_config["act_attr_to_keep"] del env_config["act_attr_to_keep"] if "backend_class" in env_config: backend_kwargs = {} if "backend_kwargs" in env_config: backend_kwargs = env_config["backend_kwargs"] del env_config["backend_kwargs"] backend = env_config["backend_class"](**backend_kwargs) del env_config["backend_class"] else: try: from lightsim2grid import LightSimBackend backend = LightSimBackend() except ImportError as exc_: from grid2op.Backend import PandaPowerBackend backend = PandaPowerBackend() # 1. create the grid2op environment self.env_glop = grid2op.make(nm_env, backend=backend, **env_config) # clean the attribute act_attr_to_keep = remove_non_usable_attr(self.env_glop, act_attr_to_keep) # 2. create the gym environment self.env_gym = GymEnv(self.env_glop) # 3. customize action space and observation space if obs_attr_to_keep is None: obs_attr_to_keep = ALL_ATTR_OBS self.env_gym.observation_space.close() self.env_gym.observation_space = BoxGymObsSpace(self.env_glop.observation_space, attr_to_keep=obs_attr_to_keep) if act_attr_to_keep is None: act_attr_to_keep = ALL_ATTR_CONT self.env_gym.action_space.close() self.env_gym.action_space = BoxGymActSpace(self.env_glop.action_space, attr_to_keep=act_attr_to_keep) # 4. specific to rllib self.action_space = Box(low=self.env_gym.action_space.low, high=self.env_gym.action_space.high, shape=self.env_gym.action_space.shape) self.observation_space = Box(low=self.env_gym.observation_space.low, high=self.env_gym.observation_space.high, shape=self.env_gym.observation_space.shape)
[docs] def reset(self, *, seed=None, options=None): tmp = self.env_gym.reset(seed=seed, options=options) return tmp
[docs] def step(self, action): return self.env_gym.step(action)