Source code for grid2op.Opponent.WeightedRandomOpponent

# Copyright (c) 2019-2020, 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 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 WeightedRandomOpponent(BaseOpponent): """ This opponent will disconnect lines randomly among the attackable lines `lines_attacked`. The sampling is weighted by the lines current usage rate divided by some factor `rho_normalization` (see init for more details). When an attack becomes possible, the time of the attack will be sampled uniformly in the next `attack_period` steps (see init). """ def __init__(self, action_space): BaseOpponent.__init__(self, action_space) self._do_nothing = None self._attacks = None self._lines_ids = None self._next_attack_time = None self._attack_period = None self._rho_normalization = None # this is the constructor: # it should have the exact same signature as here
[docs] def init(self, partial_env, lines_attacked=[], rho_normalization=[], attack_period=12*24, **kwargs): """ Generic function used to initialize the derived classes. For example, if an opponent reads from a file, the path where is the file is located should be pass with this method. Parameters ---------- lines_attacked: ``list`` The list of lines that the WeightedRandomOpponent should be able to disconnect rho_normalization: ``list`` The list of mean usage rates for the attackable lines. Should have the same length as lines_attacked. If no value is given, no normalization will be performed. The weights for sampling the attacked line are rho / rho_normalization. attack_period: ``int`` The number of steps among which the attack may happen. If attack_period=10, then whenever an attack can be made, it will happen in the 10 next steps. """ 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._do_nothing = self.action_space({}) self._attacks = [] for l_id in self._lines_ids: a = self.action_space({ 'set_line_status': [(l_id, -1)] }) self._attacks.append(a) self._attacks = np.array(self._attacks) # Usage rates normalization self._rho_normalization = np.ones_like(lines_attacked) if len(rho_normalization) == 0: warnings.warn('The usage rate normalization is not specified. No normalization will be performed.') elif len(rho_normalization) != len(lines_attacked): raise Warning(f'The usage rate normalization must have the same length as the number ' f'of attacked lines. No normalization will be performed.') else: self._rho_normalization = np.array(rho_normalization) # Opponent's attack period self._attack_period = attack_period if self._attack_period <= 0: raise OpponentError("Opponent attack cooldown need to be > 0")
[docs] def reset(self, initial_budget): self._next_attack_time = None
[docs] def tell_attack_continues(self, observation, agent_action, env_action, budget): self._next_attack_time = None
[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 """ # 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. # During creation of the environment, do not attack if observation is None: return None, 0 # Decide the time of the next attack if self._next_attack_time is None: self._next_attack_time = 1 + self.space_prng.randint(self._attack_period) self._next_attack_time -= 1 # If the attack time has not come yet, do not attack if self._next_attack_time > 0: return None, 0 # If all attackable lines are disconnected, do not attack status = observation.line_status[self._lines_ids] if not np.sum(status): return None, 0 available_attacks = self._attacks[status] rho = observation.rho[self._lines_ids][status] / self._rho_normalization[status] rho_sum = rho.sum() if rho_sum <= 0.: # this case can happen if a powerline has a flow of 0.0 but is connected, and it's the only one # that can be attacked... Pretty rare hey ! return None, 0 attack = self.space_prng.choice(available_attacks, p=rho / rho_sum) 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) new_obj._next_attack_time = copy.deepcopy(self._next_attack_time) new_obj._attack_period = copy.deepcopy(self._attack_period) new_obj._rho_normalization = copy.deepcopy(self._rho_normalization)