Source code for grid2op.Opponent.RandomLineOpponent

# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)
# 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 http://mozilla.org/MPL/2.0/.
# SPDX-License-Identifier: MPL-2.0
# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.
import warnings
import numpy as np
import copy

from grid2op.Opponent import BaseOpponent
from grid2op.Exceptions import OpponentError


[docs]class RandomLineOpponent(BaseOpponent): """ An opponent that disconnect at random any powerlines among a specified list given at the initialization. """ def __init__(self, action_space): BaseOpponent.__init__(self, action_space) self._do_nothing = None self._attacks = None self._lines_ids = None
[docs] def init(self, partial_env, lines_attacked=[], **kwargs): """ INTERNAL .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\ Used when the opponent is created. Parameters ---------- partial_env lines_attacked kwargs Returns ------- """ # this if the function used to properly set the object. # It has the generic signature above, # and it's way more flexible that the other one. if len(lines_attacked) == 0: warnings.warn( f"The opponent is deactivated as there is no information as to which line to attack. " f'You can set the argument "kwargs_opponent" to the list of the line names you want ' f' the opponent to attack in the "make" function.' ) # Store attackable lines IDs self._lines_ids = [] for l_name in lines_attacked: l_id = np.where(self.action_space.name_line == l_name) if len(l_id) and len(l_id[0]): self._lines_ids.append(l_id[0][0]) else: raise OpponentError( 'Unable to find the powerline named "{}" on the grid. For ' "information, powerlines on the grid are : {}" "".format(l_name, sorted(self.action_space.name_line)) ) # Pre-build attacks actions self._attacks = [] for l_id in self._lines_ids: att = self.action_space({"set_line_status": [(l_id, -1)]}) self._attacks.append(att) self._attacks = np.array(self._attacks)
[docs] def attack(self, observation, agent_action, env_action, budget, previous_fails): """ This method is the equivalent of "attack" for a regular agent. Opponent, in this framework can have more information than a regular agent (in particular it can view time step t+1), it has access to its current budget etc. Parameters ---------- observation: :class:`grid2op.Observation.Observation` The last observation (at time t) opp_reward: ``float`` THe opponent "reward" (equivalent to the agent reward, but for the opponent) TODO do i add it back ??? done: ``bool`` Whether the game ended or not TODO do i add it back ??? agent_action: :class:`grid2op.Action.Action` The action that the agent took env_action: :class:`grid2op.Action.Action` The modification that the environment will take. budget: ``float`` The current remaining budget (if an action is above this budget, it will be replaced by a do nothing. previous_fails: ``bool`` Wheter the previous attack failed (due to budget or ambiguous action) Returns ------- attack: :class:`grid2op.Action.Action` The attack performed by the opponent. In this case, a do nothing, all the time. duration: ``int`` The duration of the attack (if ``None`` then the attack will be made for the longest allowed time) """ # TODO maybe have a class "GymOpponent" where the observation would include the budget and all other # TODO information, and forward something to the "act" method. if observation is None: # during creation of the environment return None, 0 # i choose not to attack in this case # Status of attackable lines status = observation.line_status[self._lines_ids] # If all attackable lines are disconnected if np.all(~status): return None, 0 # i choose not to attack in this case # Pick a line among the connected lines attack = self.space_prng.choice(self._attacks[status]) return attack, None
def _custom_deepcopy_for_copy(self, new_obj, dict_=None): super()._custom_deepcopy_for_copy(new_obj, dict_) if dict_ is None: dict_ = {} new_obj._attacks = copy.deepcopy(self._attacks) new_obj._lines_ids = copy.deepcopy(self._lines_ids)