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grqnSolver.py
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grqnSolver.py
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import copy
import math
import time
import pickle
import numpy as np
from itertools import count
import matplotlib.pyplot as plt
# deep learning stuff!
import torch
import torch.nn.functional as F
import torch.nn as nn
import sys
import os
#sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) # add parent directory to file path
from lib.alegnn.utils import graphML
from lib.alegnn.modules import architecturesTime
from FaultChainSolver import FaultChainSovlerClass
class GRNNQN(nn.Module):
def __init__(self, n_actions, numNodes, dimInputSignals, dimOutputSignals, dimHiddenSignals, nFilterTaps, bias, nonlinearityHidden, nonlinearityOutput, nonlinearityReadout, dimReadout, dimEdgeFeatures):
super(GRNNQN, self).__init__()
self.n_actions = n_actions
# batchSize x timeSamples x dimReadout[-1] x numberNodes
self.grnn = architecturesTime.GraphRecurrentNN_DB_with_hidden(dimInputSignals, dimOutputSignals, dimHiddenSignals, nFilterTaps, bias, nonlinearityHidden, nonlinearityOutput, nonlinearityReadout, dimReadout, dimEdgeFeatures)
# batchSize x timeSamples x n_actions
self.out_layer = nn.Linear(numNodes*dimReadout[-1], n_actions)
# x (torch.tensor) : batchSize x timeSamples x dimInputSignals x numberNodes
# S (torch.tensor) : batchSize x timeSamples (x dimEdgeFeatures) x numberNodes x numberNodes (can ignore dimEdgeFeatures due to internal unqueeze operation!)
# ensure that "x, S, hidden" are tensors with correct dimensions!
def forward(self, x, S, hidden = None):
# make changes to the architecture so that the hidden layer is accessible from outside!
grnn_out, hidden_out = self.grnn(x, S, hidden)
# print(grnn_out.shape, hidden_out.shape)
grnn_out = grnn_out.reshape(grnn_out.shape[0], grnn_out.shape[1], -1)
# q_values : batchSize x timeSamples x n_actions
q_values = self.out_layer(grnn_out)
q_values = F.relu(q_values) # it was suprisingly performing pretty well if you don't include this as well!
return q_values, hidden_out
# "x, S, hidden" are tensors with correct dimensions!
# add an extra set which keep tracks of the already taken actions to ensure valid actions!
# act is for single time dimension and single batch!
def act(self, actionDecision, x, S, actionMask, current_round, currentState, visit_count, epsilon, hidden):
# q_values (torch.tensor) - 1 x 1 x n_actions
# hidden_out (torch.tensor) - 1 x 1 x dimHiddenSignals x numberNodes
q_values, hidden_out = self.forward(x, S, hidden)
numpyActionMaskCheck = np.array(actionMask[:, current_round])
true_idxCheck = np.argwhere(numpyActionMaskCheck)
pf_based_actions = np.intersect1d(np.array(actionDecision[:, 0], dtype=int), true_idxCheck[:, 0])
if len(pf_based_actions)==0:
# no more actions to take as sequence already taken!
return None, None, True
else:
allFalse = False
if np.random.uniform() > epsilon:
# be careful, should generate only valid actions!
# I'd like to know if all of actionMask's entries are False?!!
#print(" ")
#print("Action taken based on Q-values")
numpyActionMask = np.array(actionMask[:, current_round])
true_idx = np.argwhere(numpyActionMask)
pf_based_actions = np.intersect1d(np.array(actionDecision[:, 0], dtype=int), true_idx[:, 0])
#print("Based on GRQN: ", len(pf_based_actions))
# this one takes action greedily w.r.t Q values - Do this when do not want to store a huge table!
"""
lines_indices = torch.tensor(pf_based_actions)
max_val = torch.max(torch.index_select(q_values[0, 0, :], 0, lines_indices)).item()
action = (q_values[0, 0, :] == max_val).nonzero(as_tuple = True)[0].item()
"""
# this one takes action w.r.t Q values/visit count - Have to store a huge table if implemented naively.
maxQ = float('-Inf')
for try_action in list(pf_based_actions):
if maxQ < q_values[0, 0, try_action] / np.sqrt(visit_count[currentState, try_action] + 1):
action = try_action
maxQ = q_values[0, 0, try_action] / np.sqrt(visit_count[currentState, try_action] + 1)
else:
#print(" ")
#print("Action taken based on Power-Flow prior")
#action = self.random_action(actionMask, current_round)
action = self.power_flow_based(actionMask, current_round, actionDecision, currentState, visit_count)
hidden_next = torch.squeeze(hidden_out, 1)
return action, hidden_next, allFalse
def transition_extension_visit_decay_epsilon2(self, triallast_x, triallast_S, visit_count, EPSILON_20, NUM_ACTIONS):
q_values, _ = self.forward(triallast_x, triallast_S)
numerator = 0
denominator = 0
for try_action in range(NUM_ACTIONS):
numerator += q_values[0, 0, try_action] / np.sqrt(visit_count[0, try_action] + 1)
denominator += q_values[0, 0, try_action]
return min(EPSILON_20*numerator/denominator, 1)
def transition_extension_act(self, transition_extension_action, actionDecision, x, S, actionMask, current_round, currentState, visit_count, epsilon, hidden, epsilon2):
# q_values (torch.tensor) - 1 x 1 x n_actions
# hidden_out (torch.tensor) - 1 x 1 x dimHiddenSignals x numberNodes
q_values, hidden_out = self.forward(x, S, hidden)
numpyActionMaskCheck = np.array(actionMask[:, current_round])
true_idxCheck = np.argwhere(numpyActionMaskCheck)
pf_based_actions = np.intersect1d(np.array(actionDecision[:, 0], dtype=int), true_idxCheck[:, 0])
if len(pf_based_actions)==0:
# no more actions to take as sequence already taken!
return None, None, True
else:
allFalse = False
if np.random.uniform() > epsilon:
# be careful, should generate only valid actions!
# I'd like to know if all of actionMask's entries are False?!!
#print(" ")
#print("Action taken based on Q-values")
numpyActionMask = np.array(actionMask[:, current_round])
true_idx = np.argwhere(numpyActionMask)
pf_based_actions = np.intersect1d(np.array(actionDecision[:, 0], dtype=int), true_idx[:, 0])
# this one takes action greedily w.r.t Q values - Do this when do not want to store a huge table!
"""
lines_indices = torch.tensor(pf_based_actions)
max_val = torch.max(torch.index_select(q_values[0, 0, :], 0, lines_indices)).item()
action = (q_values[0, 0, :] == max_val).nonzero(as_tuple = True)[0].item()
"""
# this one takes action w.r.t Q values/visit count - Have to store a huge table if implemented naively.
maxQ = float('-Inf')
for try_action in list(pf_based_actions):
if maxQ < q_values[0, 0, try_action] / np.sqrt(visit_count[currentState, try_action] + 1):
action = try_action
maxQ = q_values[0, 0, try_action] / np.sqrt(visit_count[currentState, try_action] + 1)
else:
# (exploration) action according to prior knowledge
if np.random.uniform() > epsilon2:
action = self.power_flow_based(actionMask, current_round, actionDecision, currentState, visit_count)
else:
action = transition_extension_action
hidden_next = torch.squeeze(hidden_out, 1)
return action, hidden_next, allFalse
def transition_extension_q_value(self, x, S, hidden, actionDecision, actionMask, current_round, currentState, visit_count):
q_values, _ = self.forward(x, S, hidden)
numpyActionMaskCheck = np.array(actionMask[:, current_round])
true_idxCheck = np.argwhere(numpyActionMaskCheck)
pf_based_actions = np.intersect1d(np.array(actionDecision[:, 0], dtype=int), true_idxCheck[:, 0])
maxQ = float('-Inf')
for try_action in list(pf_based_actions):
if maxQ < q_values[0, 0, try_action] / np.sqrt(visit_count[currentState, try_action] + 1):
action = try_action
maxQ = q_values[0, 0, try_action] / np.sqrt(visit_count[currentState, try_action] + 1)
return action
def random_action(self, actionMask, current_round):
""" Generates RANDOM actions from (0 -- L-1)
"""
numpyActionMask = np.array(actionMask[:, current_round])
true_idx = np.argwhere(numpyActionMask)
action = np.random.choice(true_idx[:, 0])
return action
def power_flow_based(self, actionMask, current_round, actionDecision, currentState, visit_count):
""" Generates actions based on power flowing in rounds in the current round.
"""
numpyActionMask = np.array(actionMask[:, current_round])
true_idx = np.argwhere(numpyActionMask)
# true_idx reveals what actions are legal
# now find those indices that can be actually taken out
# indices of relavant lines = find intersection between true_idx[:, 0] & actionDecision[:, 0]
pf_based_actions = np.intersect1d(np.array(actionDecision[:, 0], dtype=int), true_idx[:, 0])
#print(pf_based_actions)
#print("pf_based_actions shape: ", pf_based_actions.shape)
#print("actionDecision shape: ", actionDecision.shape)
#print(actionDecision[:, 0], pf_based_actions)
relevant_rows = []
for line_index in pf_based_actions:
row_index = np.where(actionDecision[:, 0]==line_index)
relevant_rows.append(int(row_index[0]))
relevantActionDecision = actionDecision[relevant_rows, :]
visiting_count_vector = visit_count[currentState, relevant_rows]
weighted_flow = abs(relevantActionDecision[:, 2]) / np.sqrt(visiting_count_vector + 1)
action = int(relevantActionDecision[np.argmax(weighted_flow), 0])
return action
class SequentialExperienceBuffer():
""" Alternative Experience Buffer that stores sequences of fixed length
"""
def __init__(self, max_seqs, seq_len):
self.max_seqs = max_seqs
self.counter = 0
self.seq_len = seq_len
self.storage = [[] for i in range(max_seqs)] # here is where you can load the stored buffer if any - made avaiable offline!
def write_tuple(self, aoaro):
if len(self.storage[self.counter]) >= self.seq_len:
self.counter += 1
# there are many tuples recorded (that are in a sequence) in each sublist!
self.storage[self.counter].append(aoaro)
def sample(self, batch_size):
# Sample batches of (action, observation, action, reward, observation, done) tuples;
# With dimensions (batch_size, seq_len) for rewards/actions/done and (batch_size, seq_len, obs_dim) for last_Ss/last_xs;
last_Ss = []
last_xs = []
actions = []
rewards = []
Ss = []
xs = []
dones = []
for i in range(batch_size):
seq_idx = np.random.randint(self.counter)
# all of these belong to one episode!
prevDummy_S, prevDummy_x, act, rew, dummy_S, dummy_x, done = zip(*self.storage[seq_idx])
last_Ss.append(list(prevDummy_S))
last_xs.append(list(prevDummy_x))
actions.append(list(act))
rewards.append(list(rew))
Ss.append(list(dummy_S))
xs.append(list(dummy_x))
dones.append(list(done))
return torch.tensor(last_Ss, dtype = torch.float32), torch.tensor(last_xs, dtype = torch.float32), torch.tensor(actions), torch.tensor(rewards).float(), torch.tensor(Ss, dtype = torch.float32), torch.tensor(xs, dtype = torch.float32), torch.tensor(dones)
# last_Ss, last_xs : (batch_size, seq_len, obs_dim)
# actions/rewards/dones : (batch_size, seq_len)
def lastFewSamples(self, batch_size):
last_Ss = []
last_xs = []
actions = []
rewards = []
Ss = []
xs = []
dones = []
for i in range(batch_size):
seq_idx = self.counter - (batch_size + 2)
# all of these belong to one episode!
prevDummy_S, prevDummy_x, act, rew, dummy_S, dummy_x, done = zip(*self.storage[seq_idx])
last_Ss.append(list(prevDummy_S))
last_xs.append(list(prevDummy_x))
actions.append(list(act))
rewards.append(list(rew))
Ss.append(list(dummy_S))
xs.append(list(dummy_x))
dones.append(list(done))
return torch.tensor(last_Ss, dtype = torch.float32), torch.tensor(last_xs, dtype = torch.float32), torch.tensor(actions), torch.tensor(rewards).float(), torch.tensor(Ss, dtype = torch.float32), torch.tensor(xs, dtype = torch.float32), torch.tensor(dones)
# last_Ss, last_xs : (batch_size, seq_len, obs_dim)
# actions/rewards/dones : (batch_size, seq_len)
class DGRQNSolverClass(object):
def __init__(self, M_episodes, NUM_ACTIONS, LOADING_FACTOR, selectCase, dataSetString, M, EPSILON_1_m, GAMMA, numNodes, EXPLORE, replay_buffer_size, sample_length, batch_size, learning_rate, eps, dimInputSignals, dimOutputSignals, dimHiddenSignals, nFilterTaps, bias, nonlinearityHidden, nonlinearityOutput, nonlinearityReadout, dimReadout, dimEdgeFeatures, kappa):
self.M_episodes = M_episodes
self.NUM_ACTIONS = NUM_ACTIONS
self.NUM_STATES = NUM_ACTIONS*(NUM_ACTIONS-1)*(NUM_ACTIONS-2) + NUM_ACTIONS*(NUM_ACTIONS-1) + NUM_ACTIONS + 1
self.numNodes = numNodes
self.EPSILON_1_m = EPSILON_1_m
self.replay_buffer_size = replay_buffer_size
self.sample_length = sample_length
self.replay_buffer = SequentialExperienceBuffer(self.replay_buffer_size, self.sample_length)
self.LOADING_FACTOR = LOADING_FACTOR
self.selectCase = selectCase
self.FCsovler = FaultChainSovlerClass(self.LOADING_FACTOR, self.selectCase) # instantiated object!
self.visit_count = np.zeros((self.NUM_STATES, self.NUM_ACTIONS))
self.initial_state = self.FCsovler.environmentStep([])
self.dimInputSignals = dimInputSignals
self.dimOutputSignals = dimOutputSignals
self.dimHiddenSignals = dimHiddenSignals
self.nFilterTaps = nFilterTaps
self.bias = bias
self.nonlinearityHidden = nonlinearityHidden
self.nonlinearityOutput = nonlinearityOutput
self.nonlinearityReadout = nonlinearityReadout
self.dimReadout = dimReadout
self.dimEdgeFeatures = dimEdgeFeatures
self.eps = eps
self.EXPLORE = EXPLORE
self.GAMMA = GAMMA
self.batch_size = batch_size
self.learning_rate = learning_rate
self.kappa = kappa
self.answer = []
self.repeatedSequences = set()
self.numRiskyFaultChains = []
#self.numRiskyFaultChain_5percent = []
#self.numRiskyFaultChain_10percent = []
#self.numRiskyFaultChain_15percent = []
self.allFalseCount = 0
self.hiddenSequence = [None]
self.loss = []
# to keep track of the time elapsed!
self.answer_time = []
self.numRiskyFaultChains_time = []
self.TE_grnnqn = GRNNQN(self.NUM_ACTIONS, self.numNodes, self.dimInputSignals, self.dimOutputSignals, self.dimHiddenSignals, self.nFilterTaps, self.bias, self.nonlinearityHidden, self.nonlinearityOutput, self.nonlinearityReadout, self.dimReadout, self.dimEdgeFeatures)
def exp_decay_epsilon(self, eps_start, eps_end, i_episode, EXPLORE, eps_decay, MULT_DECAY):
""" float :
"""
return eps_end + (eps_start - eps_end) * math.exp((-MULT_DECAY*(i_episode-EXPLORE))/eps_decay)
def visit_decay_epsilon(self):
""" float :
"""
# [indices, capcacity, powerflow]
actionDecision = self.initial_state[5]
denominator = np.sum(actionDecision[:, 2])
numerator = np.sum(actionDecision[:, 2] / np.sqrt(self.visit_count[0, :] + 1))
#print(numerator, denominator)
return max(numerator/denominator, self.EPSILON_1_m)
def transition_extension_visit_decay_epsilon(self, EPSILON_20):
"""
"""
pass
def power_flow_based(self, actionMask, current_round, actionDecision):
#Generates actions based on power flowing in rounds in the current round.
numpyActionMaskCheck = np.array(actionMask[:, current_round])
true_idxCheck = np.argwhere(numpyActionMaskCheck) # true_idx reveals what actions are legal
pf_based_actions = np.intersect1d(np.array(actionDecision[:, 0], dtype=int), true_idxCheck[:, 0])
if len(pf_based_actions)==0:
# no more actions to take as sequence already taken!
return None, True
else:
numpyActionMask = np.array(actionMask[:, current_round])
true_idx = np.argwhere(numpyActionMask)
# now find those indices that can be actually taken out
# indices of relavant lines = find intersection between true_idx[:, 0] & actionDecision[:, 0]
pf_based_actions = np.intersect1d(np.array(actionDecision[:, 0], dtype=int), true_idx[:, 0])
#print(pf_based_actions)
#print("pf_based_actions shape: ", pf_based_actions.shape)
#print("actionDecision shape: ", actionDecision.shape)
#print(actionDecision[:, 0], pf_based_actions)
relevant_rows = []
for line_index in pf_based_actions:
row_index = np.where(actionDecision[:, 0]==line_index)
relevant_rows.append(int(row_index[0]))
relevantActionDecision = actionDecision[relevant_rows, :]
weighted_flow = abs(relevantActionDecision[:, 2])
action = int(relevantActionDecision[np.argmax(weighted_flow), 0])
return action, False
def transition_extension(self):
pass
def fill_experience_buffer(self):
for i_episode in range(self.EXPLORE):
if i_episode%20==0:
print("---------------------------")
print("Episode Number: ", i_episode, " of Collecting Experience")
# intially all actions can be taken so all are "True" since we have torch.ones!
actionMask = torch.ones((self.NUM_ACTIONS, 3), dtype=bool)
actionSpace = []
print("New Episode Action Space: ", actionSpace)
done = False
current_round = 0
current_return = 0
# reset the environment
currentAnswer = self.FCsovler.environmentStep(actionSpace)
last_S = currentAnswer[0] # np.array -- Adjacancy matrices!
last_x = currentAnswer[1] # np.array -- voltage angles!
actionDecision = currentAnswer[5]
for t in count():
# add a set which you can mutate and pass so that we can choose available actions!
action, allFalse = self.power_flow_based(actionMask, current_round, actionDecision)
if allFalse:
print("Go back to previous round number ", current_round-1 , " No more actions left...")
self.allFalseCount += 1
actionMask[:, list(range(current_round, 3))] = True
current_round -= 1
actionMask[actionSpace[-1], current_round] = False
actionSpace.pop()
currentAnswer = self.FCsovler.environmentStep(actionSpace)
actionMask[actionSpace, list(range(current_round, 3))] = False
last_S = currentAnswer[0] # np.array
last_x = currentAnswer[1] # np.array
actionDecision = currentAnswer[5]
current_return = currentAnswer[4] # reset cumulative reward until current stage!
else:
actionSpace.append(action)
print("Current Action Space: ", actionSpace)
if tuple(actionSpace) in self.repeatedSequences:
#print("Action ", actionSpace[-1], " already taken. Try another...")
# in-valid action
actionMask[actionSpace[-1], current_round] = False
actionSpace.pop()
else:
# valid action
current_round += 1
# execute action
nextAnswer = self.FCsovler.environmentStep(actionSpace)
# mark unavailable for further rounds
actionMask[actionSpace[-1], list(range(current_round, 3))] = False
# collect new observations!
S = nextAnswer[0] # np.array - GSO
x = nextAnswer[1] # np.array - Voltage Angles
reward = nextAnswer[2] # immediate scalar/reward!
done = nextAnswer[3] # boolean
actionDecision = nextAnswer[5]
current_return += reward
self.replay_buffer.write_tuple((last_S, last_x, actionSpace[-1], reward, S, x, done))
# previous observations!
last_S = copy.deepcopy(S)
last_x = copy.deepcopy(x)
if done:
break
self.repeatedSequences.add(tuple(actionSpace))
def train_grqn_already_experience(self):
self.repeatedSequences = set()
if self.selectCase == "39":
state_path = os.path.join("39-bus Code", "state_dict_for_IEEE_39.txt")
with open(state_path, "rb") as myFile:
"""
stateDict : <class 'dict'>
stateDict.keys() : tuples of sequences
stateDict.values() : int 0-93196
"""
# actionSpace to state mapping which is of length self.NUM_STATES!
stateDict = pickle.load(myFile)
elif self.selectCase == "118":
state_path = os.path.join("118-bus Code", "state_dict_for_IEEE_118.txt")
with open(state_path, "rb") as myFile:
"""
stateDict : <class 'dict'>
stateDict.keys() : tuples of sequences
stateDict.values() : int 0-120100
"""
# actionSpace to state mapping which is of length self.NUM_STATES!
stateDict = pickle.load(myFile)
grnnqn = GRNNQN(self.NUM_ACTIONS, self.numNodes, self.dimInputSignals, self.dimOutputSignals, self.dimHiddenSignals, self.nFilterTaps, self.bias, self.nonlinearityHidden, self.nonlinearityOutput, self.nonlinearityReadout, self.dimReadout, self.dimEdgeFeatures)
grnnqn_target = GRNNQN(self.NUM_ACTIONS, self.numNodes, self.dimInputSignals, self.dimOutputSignals, self.dimHiddenSignals, self.nFilterTaps, self.bias, self.nonlinearityHidden, self.nonlinearityOutput, self.nonlinearityReadout, self.dimReadout, self.dimEdgeFeatures)
grnnqn_target.load_state_dict(grnnqn.state_dict())
optimizer = torch.optim.Adam(grnnqn.parameters(), lr = self.learning_rate)
# training time performance!
for i_episode in range(self.M_episodes):
if i_episode%40==0:
print("---------------------------")
print("Episode Number: ", i_episode, " of Collecting Experience")
# intially all actions are viable, so all of actionMask are "True" since we have torch.ones!
actionMask = torch.ones((self.NUM_ACTIONS, 3), dtype=bool)
actionSpace = []
#print("New Episode Action Space: ", actionSpace)
done = False
current_round = 0
current_return = 0
# reset the environment
currentAnswer = self.FCsovler.environmentStep(actionSpace)
last_state = stateDict[tuple(actionSpace)]
last_S = currentAnswer[0] # np.array -- Adjacancy matrices!
last_x = currentAnswer[1] # np.array -- voltage angles!
actionDecision = currentAnswer[5]
# this will terminate for a length of three lines!
for t in count():
# add a set which you can mutate and pass so that we can choose available actions!
action, hidden_next, allFalse = grnnqn.act(actionDecision, torch.tensor(last_x).float().view(1, 1, 1, -1), torch.tensor(last_S).float().view(1, 1, self.numNodes, self.numNodes), actionMask, current_round, last_state, self.visit_count, epsilon = self.eps, hidden = self.hiddenSequence[-1])
if allFalse:
#print("Go back to previous round number ", current_round-1 , " No more actions left...")
self.allFalseCount += 1
actionMask[:, list(range(current_round, 3))] = True
current_round -= 1
actionMask[actionSpace[-1], current_round] = False
actionSpace.pop()
self.hiddenSequence.pop()
currentAnswer = self.FCsovler.environmentStep(actionSpace)
actionMask[actionSpace, list(range(current_round, 3))] = False
last_state = stateDict[tuple(actionSpace)]
last_S = currentAnswer[0] # np.array
last_x = currentAnswer[1] # np.array
actionDecision = currentAnswer[5]
current_return = currentAnswer[4] # reset cumulative reward until current stage!
else:
actionSpace.append(action)
self.hiddenSequence.append(hidden_next)
#print("Current Action Space: ", actionSpace)
if tuple(actionSpace) in self.repeatedSequences:
#print("Action ", actionSpace[-1], " already taken. Try another...")
# in-valid action
actionMask[actionSpace[-1], current_round] = False
actionSpace.pop()
self.hiddenSequence.pop()
else:
# update the visit_count matrix
self.visit_count[last_state, action] += 1
# valid action
current_round += 1
# execute action
nextAnswer = self.FCsovler.environmentStep(actionSpace)
# mark unavailable for further rounds
actionMask[ actionSpace[-1], list(range(current_round, 3)) ] = False
# collect new observations!
S = nextAnswer[0] # np.array - GSO
x = nextAnswer[1] # np.array - Voltage Angles
reward = nextAnswer[2] # immediate scalar/reward!
done = nextAnswer[3] # boolean
actionDecision = nextAnswer[5]
current_return += reward
current_state = stateDict[ tuple(actionSpace) ]
self.replay_buffer.write_tuple( (last_S, last_x, actionSpace[-1], reward, S, x, done) )
# previous observations!
last_S = copy.deepcopy(S)
last_x = copy.deepcopy(x)
last_state = current_state
if i_episode < 100:
self.eps = self.visit_decay_epsilon()
###### this is where the learning happens!######
if i_episode > 100:
#start = time.time()
######!######!######!######!######!######!######
for _ in range(self.kappa):
self.eps = self.visit_decay_epsilon()
# last_Ss : batch_size x timeSamples x numberNodes x numberNodes
# last_xs : batch_size x timeSamples x dimInputSignals x numberNodes
last_Ss, last_xs, actions, rewards, Ss, xs, dones = self.replay_buffer.sample(self.batch_size)
last_xs = torch.unsqueeze(last_xs, 2)
xs = torch.unsqueeze(xs, 2)
# q_values : batchSize x timeSamples x n_actions
q_values, _ = grnnqn.forward(last_xs, last_Ss)
# actions : batchSize x timeSamples
# select items form "q_values",
q_values = torch.gather(q_values, -1, actions.unsqueeze(-1)).squeeze(-1)
# predicted_q_values : batchSize x timeSamples x n_actions
predicted_q_values, _ = grnnqn_target.forward(xs, Ss)
target_values = rewards + (self.GAMMA * (1 - dones.float()) * torch.max(predicted_q_values, dim = -1)[0])
# Update network parameters
optimizer.zero_grad()
loss = torch.nn.MSELoss()(q_values , target_values.detach())
self.loss.append(loss.item())
loss.backward()
optimizer.step()
######!######!######!######!######!######!######
#end = time.time()
#print("time taken: ", end - start)
if done:
break
self.repeatedSequences.add(tuple(actionSpace))
# for plotting purpose
risky = 1 if tuple(actionSpace) in self.riskyFaultChainDict else 0
self.numRiskyFaultChains.append(risky)
#risky = 1 if tuple(actionSpace) in self.riskyFaultChainDict_all[0] else 0
#self.numRiskyFaultChain_5percent.append(risky)
#risky = 1 if tuple(actionSpace) in self.riskyFaultChainDict_all[1] else 0
#self.numRiskyFaultChain_10percent.append(risky)
#risky = 1 if tuple(actionSpace) in self.riskyFaultChainDict_all[2] else 0
#self.numRiskyFaultChain_15percent.append(risky)
self.answer.append( (round(current_return, 2), round(self.eps, 5), actionSpace) )
grnnqn_target.load_state_dict(grnnqn.state_dict())
"""
if i_episode==500:
with open("priorGRQN_for_06", "wb") as fp:
pickle.dump((grnnqn.state_dict(), self.hiddenSequence[-1]), fp)
"""
if i_episode>0 and i_episode%200==0:
print("")
print("####################")
print("GRQN: Found ", sum( self.numRiskyFaultChains ), " M% Risky FCs in ", i_episode, " search trials")
#print("GRQN: Found ", sum( self.numRiskyFaultChain_5percent ), " 5% Risky FCs in ", i_episode, " search trials")
#print("GRQN: Found ", sum( self.numRiskyFaultChain_10percent ), " 10% Risky FCs in ", i_episode, " search trials")
#print("GRQN: Found ", sum( self.numRiskyFaultChain_15percent ), " 15% Risky FCs in ", i_episode, " search trials")
self.compute_rewards()
print("GRQN: The cumulative risk is ", sum(self.rewards))
print("GRQN: Number of ALL False conditions: ", self.allFalseCount)
print("####################")
print("")
def train_grqn_already_experience_time(self, time_taken):
self.repeatedSequences = set()
if self.selectCase == "39":
state_path = os.path.join("39-bus Code", "state_dict_for_IEEE_39.txt")
with open(state_path, "rb") as myFile:
"""
stateDict : <class 'dict'>
stateDict.keys() : tuples of sequences
stateDict.values() : int 0-93196
"""
# actionSpace to state mapping which is of length self.NUM_STATES!
stateDict = pickle.load(myFile)
elif self.selectCase == "118":
state_path = os.path.join("118-bus Code", "state_dict_for_IEEE_118.txt")
with open(state_path, "rb") as myFile:
"""
stateDict : <class 'dict'>
stateDict.keys() : tuples of sequences
stateDict.values() : int 0-120100
"""
# actionSpace to state mapping which is of length self.NUM_STATES!
stateDict = pickle.load(myFile)
grnnqn = GRNNQN(self.NUM_ACTIONS, self.numNodes, self.dimInputSignals, self.dimOutputSignals, self.dimHiddenSignals, self.nFilterTaps, self.bias, self.nonlinearityHidden, self.nonlinearityOutput, self.nonlinearityReadout, self.dimReadout, self.dimEdgeFeatures)
grnnqn_target = GRNNQN(self.NUM_ACTIONS, self.numNodes, self.dimInputSignals, self.dimOutputSignals, self.dimHiddenSignals, self.nFilterTaps, self.bias, self.nonlinearityHidden, self.nonlinearityOutput, self.nonlinearityReadout, self.dimReadout, self.dimEdgeFeatures)
grnnqn_target.load_state_dict(grnnqn.state_dict())
optimizer = torch.optim.Adam(grnnqn.parameters(), lr = self.learning_rate)
# training time performance!
self.i_episode = 0
start = time.perf_counter()
while time.perf_counter() - start < time_taken:
self.i_episode += 1
print("---------------------------")
print("Episode number: ", self.i_episode)
# intially all actions can be taken so all are "True" since we have torch.ones!
actionMask = torch.ones((self.NUM_ACTIONS, 3), dtype=bool)
actionSpace = []
print("New Episode Action Space: ", actionSpace)
done = False
current_round = 0
current_return = 0
# reset the environment
currentAnswer = self.FCsovler.environmentStep(actionSpace)
last_state = stateDict[tuple(actionSpace)]
last_S = currentAnswer[0] # np.array -- Adjacancy matrices!
last_x = currentAnswer[1] # np.array -- voltage angles!
actionDecision = currentAnswer[5]
# this will terminate for a length of three lines!
for t in count():
# add a set which you can mutate and pass so that we can choose available actions!
action, hidden_next, allFalse = grnnqn.act(actionDecision, torch.tensor(last_x).float().view(1, 1, 1, -1), torch.tensor(last_S).float().view(1, 1, self.numNodes, self.numNodes), actionMask, current_round, last_state, self.visit_count, epsilon = self.eps, hidden = self.hiddenSequence[-1])
if allFalse:
print("Go back to previous round number ", current_round-1 , " No more actions left...")
self.allFalseCount += 1
actionMask[:, list(range(current_round, 3))] = True
current_round -= 1
actionMask[actionSpace[-1], current_round] = False
actionSpace.pop()
self.hiddenSequence.pop()
currentAnswer = self.FCsovler.environmentStep(actionSpace)
actionMask[actionSpace, list(range(current_round, 3))] = False
last_state = stateDict[tuple(actionSpace)]
last_S = currentAnswer[0] # np.array
last_x = currentAnswer[1] # np.array
actionDecision = currentAnswer[5]
current_return = currentAnswer[4] # reset cumulative reward until current stage!
else:
actionSpace.append(action)
self.hiddenSequence.append(hidden_next)
print("Current Action Space: ", actionSpace)
if tuple(actionSpace) in self.repeatedSequences:
#print("Action ", actionSpace[-1], " already taken. Try another...")
# in-valid action
actionMask[actionSpace[-1], current_round] = False
actionSpace.pop()
self.hiddenSequence.pop()
else:
# update the visit_count matrix
self.visit_count[last_state, action] += 1
# valid action
current_round += 1
# execute action
nextAnswer = self.FCsovler.environmentStep(actionSpace)
# mark unavailable for further rounds
actionMask[actionSpace[-1], list(range(current_round, 3))] = False
# collect new observations!
S = nextAnswer[0] # np.array - GSO
x = nextAnswer[1] # np.array - Voltage Angles
reward = nextAnswer[2] # immediate scalar/reward!
done = nextAnswer[3] # boolean
actionDecision = nextAnswer[5]
current_return += reward
current_state = stateDict[tuple(actionSpace)]
self.replay_buffer.write_tuple((last_S, last_x, actionSpace[-1], reward, S, x, done))
# previous observations!
last_S = copy.deepcopy(S)
last_x = copy.deepcopy(x)
last_state = current_state
###### this is where the learning happens!######
######!######!######!######!######!######!######
for _ in range(self.kappa):
self.eps = self.visit_decay_epsilon()
# last_Ss : batch_size x timeSamples x numberNodes x numberNodes
# last_xs : batch_size x timeSamples x dimInputSignals x numberNodes
last_Ss, last_xs, actions, rewards, Ss, xs, dones = self.replay_buffer.sample(self.batch_size)
last_xs = torch.unsqueeze(last_xs, 2)
xs = torch.unsqueeze(xs, 2)
# q_values : batchSize x timeSamples x n_actions
q_values, _ = grnnqn.forward(last_xs, last_Ss)
# actions : batchSize x timeSamples
# select items form "q_values",
q_values = torch.gather(q_values, -1, actions.unsqueeze(-1)).squeeze(-1)
# predicted_q_values : batchSize x timeSamples x n_actions
predicted_q_values, _ = grnnqn_target.forward(xs, Ss)
target_values = rewards + (self.GAMMA * (1 - dones.float()) * torch.max(predicted_q_values, dim = -1)[0])
# Update network parameters
optimizer.zero_grad()
loss = torch.nn.MSELoss()(q_values , target_values.detach())
self.loss.append(loss.item())
loss.backward()
optimizer.step()
######!######!######!######!######!######!######
if done:
break
stamp = time.perf_counter() - start
self.repeatedSequences.add(tuple(actionSpace))
# for plotting purpose
risky = 1 if tuple(actionSpace) in self.riskyFaultChainDict else 0
self.numRiskyFaultChains.append(risky)
self.answer.append((round(current_return, 2), round(self.eps, 5), actionSpace))
grnnqn_target.load_state_dict(grnnqn.state_dict())
# keeping track of the time module!
self.numRiskyFaultChains_time.append([risky, round(stamp, 3)])
self.answer_time.append([(round(current_return, 2), round(self.eps, 5), actionSpace), round(stamp, 3)])
def transition_extension_train_grqn_already_experience(self, EPSILON_20):
self.repeatedSequences = set()
with open("state_dict_for_IEEE_39.txt", "rb") as myFile:
""" stateDict : <class 'dict'>
stateDict.keys() : tuples of sequences
stateDict.values() : int 0-93196
"""
# actionSpace to state mapping which is of length self.NUM_STATES!
stateDict = pickle.load(myFile)
with open("priorGRQN_for_06", "rb") as myFile:
"""
"""
# actionSpace to state mapping which is of length self.NUM_STATES!
trans_ext_state, hidden = pickle.load(myFile)
transition_extension_grnnqn = GRNNQN(self.NUM_ACTIONS, self.numNodes, self.dimInputSignals, self.dimOutputSignals, self.dimHiddenSignals, self.nFilterTaps, self.bias, self.nonlinearityHidden, self.nonlinearityOutput, self.nonlinearityReadout, self.dimReadout, self.dimEdgeFeatures)
transition_extension_grnnqn.load_state_dict(trans_ext_state)
grnnqn = GRNNQN(self.NUM_ACTIONS, self.numNodes, self.dimInputSignals, self.dimOutputSignals, self.dimHiddenSignals, self.nFilterTaps, self.bias, self.nonlinearityHidden, self.nonlinearityOutput, self.nonlinearityReadout, self.dimReadout, self.dimEdgeFeatures)
grnnqn_target = GRNNQN(self.NUM_ACTIONS, self.numNodes, self.dimInputSignals, self.dimOutputSignals, self.dimHiddenSignals, self.nFilterTaps, self.bias, self.nonlinearityHidden, self.nonlinearityOutput, self.nonlinearityReadout, self.dimReadout, self.dimEdgeFeatures)
grnnqn_target.load_state_dict(grnnqn.state_dict())
optimizer = torch.optim.Adam(grnnqn.parameters(), lr = self.learning_rate)
# training time performance!
for i_episode in range(self.M_episodes):
print("---------------------------")
print("Episode number: ", i_episode)
# intially all actions can be taken so all are "True" since we have torch.ones!
actionMask = torch.ones((self.NUM_ACTIONS, 3), dtype=bool)
actionSpace = []
print("New Episode Action Space: ", actionSpace)
done = False
current_round = 0
current_return = 0
# reset the environment
currentAnswer = self.FCsovler.environmentStep(actionSpace)
last_state = stateDict[tuple(actionSpace)]
last_S = currentAnswer[0] # np.array -- Adjacancy matrices!
last_x = currentAnswer[1] # np.array -- voltage angles!
actionDecision = currentAnswer[5]
# this will terminate for a length of three lines!
for t in count():
triallast_S = self.initial_state[0] # np.array -- Adjacancy matrices!
triallast_x = self.initial_state[1] # np.array -- voltage angles!
eps2 = grnnqn.transition_extension_visit_decay_epsilon2(torch.tensor(triallast_x).float().view(1, 1, 1, -1), torch.tensor(triallast_S).float().view(1, 1, self.numNodes, self.numNodes), self.visit_count, EPSILON_20, self.NUM_ACTIONS)
transition_extension_action = transition_extension_grnnqn.transition_extension_q_value(torch.tensor(last_x).float().view(1, 1, 1, -1), torch.tensor(last_S).float().view(1, 1, self.numNodes, self.numNodes), None, actionDecision, actionMask, current_round, last_state, self.visit_count)
# add a set which you can mutate and pass so that we can choose available actions!
action, hidden_next, allFalse = grnnqn.transition_extension_act(transition_extension_action, actionDecision, torch.tensor(last_x).float().view(1, 1, 1, -1), torch.tensor(last_S).float().view(1, 1, self.numNodes, self.numNodes), actionMask, current_round, last_state, self.visit_count, epsilon = self.eps, hidden = self.hiddenSequence[-1], epsilon2 = eps2)
if allFalse:
print("Go back to previous round number ", current_round-1 , " No more actions left...")
self.allFalseCount += 1
actionMask[:, list(range(current_round, 3))] = True
current_round -= 1
actionMask[actionSpace[-1], current_round] = False
actionSpace.pop()
self.hiddenSequence.pop()
currentAnswer = self.FCsovler.environmentStep(actionSpace)
actionMask[actionSpace, list(range(current_round, 3))] = False
last_state = stateDict[tuple(actionSpace)]
last_S = currentAnswer[0] # np.array
last_x = currentAnswer[1] # np.array
actionDecision = currentAnswer[5]
current_return = currentAnswer[4] # reset cumulative reward until current stage!
else:
actionSpace.append(action)
self.hiddenSequence.append(hidden_next)
print("Current Action Space: ", actionSpace)
if tuple(actionSpace) in self.repeatedSequences:
#print("Action ", actionSpace[-1], " already taken. Try another...")
# in-valid action
actionMask[actionSpace[-1], current_round] = False
actionSpace.pop()
self.hiddenSequence.pop()
else:
# update the visit_count matrix
self.visit_count[last_state, action] += 1
self.eps = self.visit_decay_epsilon()
# valid action
current_round += 1
# execute action
nextAnswer = self.FCsovler.environmentStep(actionSpace)
# mark unavailable for further rounds
actionMask[actionSpace[-1], list(range(current_round, 3))] = False
# collect new observations!
S = nextAnswer[0] # np.array - GSO
x = nextAnswer[1] # np.array - Voltage Angles
reward = nextAnswer[2] # immediate scalar/reward!
done = nextAnswer[3] # boolean
actionDecision = nextAnswer[5]
current_return += reward
current_state = stateDict[tuple(actionSpace)]
self.replay_buffer.write_tuple((last_S, last_x, actionSpace[-1], reward, S, x, done))
# previous observations!
last_S = copy.deepcopy(S)
last_x = copy.deepcopy(x)
last_state = current_state
###### this is where the learning happens!######
######!######!######!######!######!######!######
for _ in range(3):
# last_Ss : batch_size x timeSamples x numberNodes x numberNodes
# last_xs : batch_size x timeSamples x dimInputSignals x numberNodes
last_Ss, last_xs, actions, rewards, Ss, xs, dones = self.replay_buffer.sample(self.batch_size)
last_xs = torch.unsqueeze(last_xs, 2)
xs = torch.unsqueeze(xs, 2)
# q_values : batchSize x timeSamples x n_actions
q_values, _ = grnnqn.forward(last_xs, last_Ss)
# actions : batchSize x timeSamples
# select items form "q_values",
q_values = torch.gather(q_values, -1, actions.unsqueeze(-1)).squeeze(-1)
# predicted_q_values : batchSize x timeSamples x n_actions
predicted_q_values, _ = grnnqn_target.forward(xs, Ss)
target_values = rewards + (self.GAMMA * (1 - dones.float()) * torch.max(predicted_q_values, dim = -1)[0])
# Update network parameters
optimizer.zero_grad()
loss = torch.nn.MSELoss()(q_values , target_values.detach())
self.loss.append(loss.item())
loss.backward()
optimizer.step()
######!######!######!######!######!######!######
if done:
break
self.repeatedSequences.add(tuple(actionSpace))
# for plotting purpose
risky = 1 if tuple(actionSpace) in self.riskyFaultChainDict else 0
self.numRiskyFaultChains.append(risky)
self.answer.append((round(current_return, 2), round(self.eps, 5), actionSpace))
grnnqn_target.load_state_dict(grnnqn.state_dict())
def train_grqn(self):
"""