def __init__(self, action_space, observation_space):
"""Initialize a new agent."""
BaseAgent.__init__(self, action_space=action_space)
#from amorot
#super().__init__(action_space)
#self.name = name
#self.grid = gridName # IEEE14,IEEE118_R2 (WCCI or Neurips Track Robustness), IEEE118
#logging.info("the grid you indicated to the Expert System is:" + gridName)
self.curr_iter = 0
self.sub_2nodes = set()
self.lines_disconnected = set()
self.action_space = action_space
self.observation_space = observation_space
self.threshold_powerFlow_safe = 0.95
self.maxOverloadsAtATime = 3 # We should not run it more than
self.config = {
"totalnumberofsimulatedtopos": 25,
"numberofsimulatedtopospernode": 5,
"maxUnusedLines": 2,
"ratioToReconsiderFlowDirection": 0.75,
"ratioToKeepLoop": 0.25,
"ThersholdMinPowerOfLoop": 0.1,
"ThresholdReportOfLine": 0.2
}
self.reward_type = "MinMargin_reward" # "MinMargin_reward"#we use the L2RPN reward to score the topologies, not the interal alphadeesp score
self.action_space = action_space
def __init__(self, action_space):
"""Initialize a new agent."""
BaseAgent.__init__(self, action_space=action_space)
self.state_size = 943 + 177
self.action_size = 987 + 21
self.hidden1, self.hidden2, self.hidden3 = 1000, 1000, 1000
self.actor = self.build_model()
self.load_model('pypow_wcci_a3c_backup')
print("Loaded saved NN model parameters \n")
#self.backup_agent2 = Backup_agent2(action_space)
tf.get_default_graph()
#self.sess = tf.InteractiveSession()
#K.set_session(self.sess)
#self.sess = tf.InteractiveSession()
# TF 1.x - sess = tf.InteractiveSession(); TF 2.X sess=tf.compat.v1.InteractiveSession()
#K.set_session(self.sess) # tensorflow 1.X
#tf.compat.v1.keras.backend.set_session(self.sess) # tensorflow 2.X
#tf.compat.v1.disable_eager_execution() # compatibility issues due to tf 2.0
#self.sess.run(tf.global_variables_initializer()) # tensorflow 1.X
self.tested_action = None
def __init__(self, action_space,net_dict,effective_topo,sub_info):
"""Initialize a new agent."""
BaseAgent.__init__(self, action_space=action_space)
self.policy_net = Network()
self.policy_net.load_state_dict(net_dict)
self.policy_net.eval()
self.effective_topo = effective_topo
self.sub_info = sub_info
def __init__(self, action_space, observation_space):
"""Initialize a new agent."""
BaseAgent.__init__(self, action_space=action_space)
#super().__init__(action_space)
#self.name = name
#self.grid = gridName # IEEE14,IEEE118_R2 (WCCI or Neurips Track Robustness), IEEE118
#logging.info("the grid you indicated to the Expert System is:" + gridName)
self.curr_iter = 0
self.sub_2nodes = set()
self.lines_disconnected = set()
self.action_space = action_space
self.observation_space = observation_space
self.threshold_powerFlow_safe = 0.95
self.maxOverloadsAtATime = 3 # We should not run it more than
self.config = {
"totalnumberofsimulatedtopos": 25,
"numberofsimulatedtopospernode": 5,
"maxUnusedLines": 2,
"ratioToReconsiderFlowDirection": 0.75,
"ratioToKeepLoop": 0.25,
"ThersholdMinPowerOfLoop": 0.1,
"ThresholdReportOfLine": 0.2
}
self.reward_type = "MinMargin_reward" # "MinMargin_reward"#we use the L2RPN reward to score the topologies, not the interal alphadeesp score
self.nline = 186
self.ngen = 22
self.controllablegen = {0, 2, 3, 4, 10, 13, 16, 19, 20, 21}
self.redispatchable = np.bool([
True, False, True, True, True, False, False, False, False, False,
True, False, False, True, False, False, True, False, False, True,
True, True
])
self.timestep = 0
self.target_dispatch = np.zeros(self.ngen)
self.base_power = [
48, 28.2, 0, 150, 50, 0, 0, 0, 0, 0, 47.6, 0, 0, 70, 0, 0, 98.9, 0,
0, 300, 51, 180
]
self.lines_attacked = [0, 9, 13, 14, 18, 23, 27, 39, 45, 56]
self.lines_cut = set()
self.thermal_limits = [
60.9, 231.9, 272.6, 212.8, 749.2, 332.4, 348., 414.4, 310.1, 371.4,
401.2, 124.3, 298.5, 86.4, 213.9, 160.8, 112.2, 291.4, 489., 489.,
124.6, 196.7, 191.9, 238.4, 174.2, 105.6, 143.7, 293.4, 288.9,
107.7, 415.5, 148.2, 124.2, 154.4, 85.9, 106.5, 142., 124., 130.2,
86.2, 278.1, 182., 592.1, 173.1, 249.8, 441., 344.2, 722.8, 494.6,
494.6, 196.7, 151.8, 263.4, 364.1, 327.
]
self.operationsequence = np.zeros(self.nline)
self.recoversequence = np.zeros(self.nline)
self.action_space = action_space
#self.observation_space = observation_space
self.threshold_powerFlow_safe = 0.95
def __init__(
self,
actor, # the agent that will take some actions
proxy, # the proxy to train / evaluate
logdir=None, # tensorboard logs
update_tensorboard=256, # tensorboard is updated every XXX training iterations
save_freq=int(1024) *
int(64), # model is saved every save_freq training iterations
ext=".h5", # extension of the file in which you want to save the proxy
nb_obs_init=256, # number of observations that are sent to the proxy to be initialized
):
BaseAgent.__init__(self, actor.action_space)
self.actor = actor
# to fill the training / test dataset
self.global_iter = 0
self.train_iter = 0
self.__is_init = False # is this model initiliazed
self.is_training = True
self._nb_obs_init = nb_obs_init
# proxy part
self._proxy = proxy
# tensorboard (should be initialized after the proxy)
if logdir is not None:
logpath = os.path.join(logdir, self.get_name())
self._tf_writer = tf.summary.create_file_writer(
logpath, name=self.get_name())
else:
self._tf_writer = None
self.update_tensorboard = update_tensorboard
self.save_freq = int(save_freq)
# save / load
if re.match(r"^\.", ext) is None:
# add a point at the beginning of the extension
self.ext = f".{ext}"
else:
self.ext = ext
self.save_path = None
def __init__(self, action_space):
"""Initialize a new agent."""
BaseAgent.__init__(self, action_space=action_space)
self.action_space = action_space
self.device = torch.device(
"cuda:" + str(0) if torch.cuda.is_available() else "cpu")
self.obs_size = 1040
self.act_size = 386
self.start_epoch = 1
self.distr_params = {"num_bins": 51, "v_min": -1.0, "v_max": 1.0}
self.prioritized_params = {"a": 0.6, "b": 0.6, "eps": 1e-5}
self.distr_params["v_range"] = torch.linspace(
self.distr_params["v_min"], self.distr_params["v_max"],
self.distr_params["num_bins"]).to(self.device)
self.model = DuelingDistributionalNetwork(self.obs_size, self.act_size,
self.distr_params,
True).to(self.device)
self.optimizer = optim.Adam(self.model.parameters(), lr=0.0001)
self.load_weights()
def __init__(self, action_space, observation_space):
"""Initialize a new agent."""
BaseAgent.__init__(self, action_space=action_space)
#from amarot
self.sub_2nodes = set()
self.lines_disconnected = set()
self.action_space = action_space
self.observation_space = observation_space
self.threshold_powerFlow_safe = 0.95
self.maxOverloadsAtATime = 3 # We should not run it more than
self.config = {
"totalnumberofsimulatedtopos": 25,
"numberofsimulatedtopospernode": 5,
"maxUnusedLines": 2,
"ratioToReconsiderFlowDirection": 0.75,
"ratioToKeepLoop": 0.25,
"ThersholdMinPowerOfLoop": 0.1,
"ThresholdReportOfLine": 0.2
}
self.reward_type = "MinMargin_reward" # "MinMargin_reward"#we use the L2RPN reward to score the topologies, not the interal alphadeesp score
self.action_space = action_space
def __init__(self, action_space, this_directory_path='./'):
"""Initialize a new agent."""
BaseAgent.__init__(self, action_space=action_space)
self.actions62 = np.load(
os.path.join(this_directory_path, 'actions62.npy'))
self.actions146 = np.load(
os.path.join(this_directory_path, 'actions146.npy'))
self.actions = np.concatenate((self.actions62, self.actions146),
axis=0)
self.actions1255 = np.load(
os.path.join(this_directory_path, 'actions1255.npy'))
chosen = list(range(2, 7)) + list(range(7, 73)) + list(range(
73, 184)) + list(range(184, 656))
chosen += list(range(656, 715)) + list(range(715, 774)) + list(
range(774, 833)) + list(range(833, 1010))
chosen += list(range(1010, 1069)) + list(range(1069, 1105)) + list(
range(1105, 1164)) + list(range(1164, 1223))
self.chosen = chosen
self.ppo = tf.keras.models.load_model(
os.path.join(this_directory_path, './ppo-ckpt'))
self.last_step = datetime.datetime.now()
self.recovery_stack = []
self.overflow_steps = 0
def __init__(self,
action_space,
env,
n_gens_to_Redispatch=2,
redispatching_increment=1):
"""
Initialize agent
:param action_space: the Grid2Op action space
:param n_gens_to_Redispatch: the maximum number of dispatchable generators to play with
:param redispatching_increment: the redispatching MW value to play with (both Plus or Minus)
"""
BaseAgent.__init__(self, action_space)
self.desired_actions = []
# we create a dictionnary of redispatching actions we want to play with
GensToRedipsatch = [
i for i in range(len(env.gen_redispatchable))
if env.gen_redispatchable[i]
]
if len(GensToRedipsatch) > n_gens_to_Redispatch:
GensToRedipsatch = GensToRedipsatch[0:n_gens_to_Redispatch]
# action dic will have 2 actions par generator (increase or decrease by the increment) + do nothing
self.desired_actions.append(self.action_space({})) # do_nothing action
for i in GensToRedipsatch:
# redispatching decreasing the production by the increment
act1 = self.action_space(
{"redispatch": [(i, -redispatching_increment)]})
self.desired_actions.append(act1)
# redispatching increasing the production by the increment
act2 = self.action_space(
{"redispatch": [(i, +redispatching_increment)]})
self.desired_actions.append(act2)
def __init__(self, action_space):
BaseAgent.__init__(self, action_space)
def __init__(self, action_space):
"""Initialize a new agent."""
BaseAgent.__init__(self, action_space=action_space)
def __init__(self, action_space, this_directory_path):
BaseAgent.__init__(self, action_space=action_space)
self.time_step = 0
self.lasttime = datetime.datetime.now()
self.actions = np.load(
os.path.join(this_directory_path, 'actions1_109.npy')) #
self.actions2 = np.load(
os.path.join(this_directory_path, 'actions2_124.npy'))
self.substation_g1 = np.zeros(len(self.actions), dtype=np.int16)
self.substation_g2 = np.zeros(len(self.actions2), dtype=np.int16)
self.actionstack = []
self.sub_item1 = []
self.sub_item2 = []
for idx, a in enumerate(self.actions):
action = self.action_space(
{'change_bus': self.actions[idx][719:1252]})
action._change_bus_vect = action._change_bus_vect.astype(bool)
ta = action.as_dict()
self.substation_g1[idx] = int(
ta['change_bus_vect']['modif_subs_id'][0])
self.sub_item1.append(ta['change_bus_vect'][str(
self.substation_g1[idx])].items())
for idx, a in enumerate(self.actions2):
action = self.action_space(
{'change_bus': self.actions2[idx][719:1252]})
action._change_bus_vect = action._change_bus_vect.astype(bool)
ta = action.as_dict()
self.substation_g2[idx] = int(
ta['change_bus_vect']['modif_subs_id'][0])
self.sub_item2.append(ta['change_bus_vect'][str(
self.substation_g2[idx])].items())
self.dict = {
0: [0, 1, 2, 4, 6, 10, 11, 13, 15, 116],
1: [0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 13, 15, 116],
2: [0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 116],
3: [67, 68, 69, 70, 71, 72, 73, 74, 76, 46, 48, 117, 22, 23],
4: [68, 69, 70, 71, 72, 73, 74, 21, 22, 23, 24, 31],
5: [68, 69, 70, 71, 72, 73, 74, 22, 23],
6: [68, 69, 70, 71, 72, 73, 74, 23],
7: [67, 68, 69, 70, 71, 72, 73, 74, 76, 46, 48, 117, 22, 23],
8: [
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 46, 79, 48, 81,
117, 22, 23
],
9: [
23, 41, 44, 45, 46, 47, 48, 49, 50, 53, 64, 65, 67, 68, 69, 70,
73, 74, 75, 76, 77, 79, 80, 81, 115, 117
],
10: [67, 68, 69, 70, 73, 74, 75, 76, 77, 46, 79, 48, 81, 117, 23],
11: [
46, 48, 67, 68, 69, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 95,
96, 97, 98, 117
],
12: [
23, 41, 44, 45, 46, 47, 48, 49, 50, 53, 64, 65, 67, 68, 69, 70,
73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 95, 96, 97, 98, 115,
117
],
13: [0, 1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, 15, 16, 116],
14: [
23, 46, 48, 67, 68, 69, 70, 73, 74, 75, 76, 77, 78, 79, 80, 81,
82, 95, 96, 97, 98, 117
],
15: [
46, 48, 67, 68, 69, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 95,
96, 97, 98, 117
],
16: [96, 97, 98, 68, 74, 75, 76, 77, 78, 79, 80, 81, 95],
17: [
46, 48, 67, 68, 69, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 93,
94, 95, 96, 97, 98, 99, 117
],
18: [
46, 48, 67, 68, 69, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 93,
94, 95, 96, 97, 98, 99, 117
],
19: [
96, 97, 98, 67, 68, 99, 74, 75, 76, 77, 78, 79, 80, 81, 93, 94,
95
],
20: [
46, 48, 67, 68, 69, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 93, 94, 95, 96, 97, 98, 117
],
21: [
96, 68, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 88, 93, 94,
95
],
22: [76, 81, 82, 83, 84, 85, 87, 88, 95],
23: [76, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 95],
24: [0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 116],
25: [81, 82, 83, 84, 85, 86, 87, 88, 89, 91],
26: [81, 82, 83, 84, 85, 86, 87, 88, 89, 91],
27: [81, 82, 83, 84, 85, 86, 87, 88, 89, 91],
28: [99, 101, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93],
29: [99, 101, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 93],
30: [99, 101, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 93],
31: [99, 101, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 93],
32: [99, 101, 84, 87, 88, 89, 90, 91, 92, 93],
33: [
82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 97, 98, 99,
100, 101, 102, 103, 105
],
34: [
82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 97, 98, 99,
100, 101, 102, 103, 105
],
35: [0, 1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, 15, 16, 116],
36: [
97, 98, 99, 100, 101, 102, 103, 105, 84, 87, 88, 89, 90, 91,
92, 93, 94, 95
],
37: [
97, 98, 99, 100, 101, 102, 103, 105, 84, 87, 88, 89, 90, 91,
92, 93, 94, 95
],
38: [
79, 81, 84, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100, 101, 102, 103, 105
],
39: [
96, 97, 98, 99, 100, 101, 102, 103, 105, 79, 81, 88, 90, 91,
92, 93, 94, 95
],
40: [
96, 97, 98, 99, 100, 101, 102, 103, 105, 79, 81, 88, 90, 91,
92, 93, 94, 95
],
41: [
67, 68, 74, 75, 76, 77, 78, 79, 80, 81, 82, 91, 92, 93, 94, 95,
96, 97, 98, 99
],
42: [
68, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 91, 92, 93, 94,
95, 96, 97, 98, 99
],
43: [
76, 78, 79, 80, 81, 82, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98,
99, 100, 101, 102, 103, 105
],
44: [
96, 97, 98, 67, 68, 99, 74, 75, 76, 77, 78, 79, 80, 81, 93, 94,
95
],
45: [
67, 68, 74, 75, 76, 77, 78, 79, 80, 81, 91, 93, 94, 95, 96, 97,
98, 99, 100, 102, 103, 105
],
46: [32, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 18, 116],
47: [
67, 68, 74, 75, 76, 77, 78, 79, 80, 81, 91, 93, 94, 95, 96, 97,
98, 99, 100, 102, 103, 105
],
48: [
79, 84, 87, 88, 89, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100,
101, 102, 103, 104, 105, 106, 109
],
49: [
79, 81, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101,
102, 103, 104, 105, 106, 109
],
50: [96, 97, 98, 99, 76, 78, 79, 80, 81, 82, 91, 92, 93, 94, 95],
51: [96, 97, 98, 99, 76, 78, 79, 80, 81, 82, 91, 92, 93, 94, 95],
52: [
76, 78, 79, 80, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100, 101, 102, 103, 104, 105, 106, 109
],
53: [
76, 78, 79, 80, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100, 101, 102, 103, 104, 105, 106, 109
],
54: [
79, 88, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103,
104, 105, 106, 109
],
55: [
97, 98, 99, 100, 101, 102, 103, 105, 84, 87, 88, 89, 90, 91,
92, 93, 94, 95
],
56: [97, 98, 99, 100, 101, 102, 103, 105, 88, 90, 91, 92, 93],
57: [0, 1, 2, 3, 4, 5, 6, 32, 10, 11, 12, 13, 14, 15, 16, 18, 116],
58: [
79, 88, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103,
104, 105, 106, 107, 108, 109, 110, 111
],
59: [
79, 88, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103,
104, 105, 106, 107, 109
],
60: [
97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110,
111, 91, 93
],
61: [
97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110,
111, 91, 93
],
62: [
79, 88, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103,
104, 105, 106, 107, 109
],
63:
[97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 91, 93],
64:
[97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 91, 93],
65: [99, 102, 103, 104, 105, 106, 107, 108, 109],
66: [99, 102, 103, 104, 105, 106, 107, 108, 109],
67: [97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 91, 93],
68: [
32, 33, 3, 4, 36, 10, 11, 12, 13, 14, 15, 16, 17, 18, 112, 19,
29, 30
],
69: [102, 103, 104, 105, 106, 107, 108, 109, 110, 111],
70: [
97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110,
111, 91, 93
],
71: [99, 102, 103, 104, 107, 108, 109, 110, 111],
72: [99, 102, 103, 104, 107, 108, 109, 110, 111],
73: [99, 102, 103, 104, 107, 108, 109, 110, 111],
74: [
7, 11, 12, 13, 14, 15, 16, 17, 18, 22, 25, 26, 28, 29, 30, 31,
32, 37, 112, 113
],
75: [
14, 15, 112, 16, 113, 17, 114, 21, 22, 23, 24, 26, 27, 28, 29,
30, 31
],
76: [112, 113, 114, 16, 21, 22, 23, 24, 26, 27, 28, 30, 31],
77: [112, 113, 114, 22, 24, 25, 26, 27, 28, 30, 31],
78: [112, 113, 114, 22, 24, 26, 27, 30, 31],
79: [
1, 2, 6, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 29, 30, 32,
33, 36, 112, 116
],
80: [0, 1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, 15, 16, 116],
81: [67, 68, 69, 70, 73, 74, 75, 76, 77, 46, 79, 48, 81, 117, 23],
82: [68, 69, 73, 74, 75, 76, 77, 79, 81, 117],
83: [
0, 1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, 15, 16, 17, 29, 30,
112, 116
],
84: [
7, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 25, 28, 29, 30, 31,
32, 33, 36, 37, 112
],
85: [0, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 29],
86: [
1, 2, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 18, 25, 28, 29, 30,
31, 32, 37, 112, 116
],
87: [
7, 11, 12, 13, 14, 15, 16, 17, 18, 19, 25, 28, 29, 30, 31, 32,
33, 37, 112
],
88: [
32, 33, 35, 36, 42, 12, 13, 14, 15, 16, 17, 18, 19, 112, 20,
29, 30
],
89: [32, 33, 35, 36, 42, 12, 13, 14, 16, 17, 18, 19, 20, 21],
90: [
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 29, 30, 32, 33, 35,
36, 42, 112
],
91: [33, 14, 17, 18, 19, 20, 21, 22],
92: [18, 19, 20, 21, 22, 23, 24, 31],
93: [69, 71, 112, 113, 19, 20, 21, 22, 23, 24, 25, 26, 30, 31],
94: [
68, 69, 70, 71, 73, 74, 112, 113, 20, 21, 22, 23, 24, 25, 26,
30, 31
],
95: [
69, 71, 112, 113, 114, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30,
31
],
96: [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 116, 29],
97: [112, 113, 114, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
98: [112, 113, 114, 22, 24, 25, 26, 27, 28, 30, 31],
99: [16, 114, 24, 26, 27, 28, 30, 31],
100: [
2, 3, 4, 5, 7, 8, 9, 10, 14, 15, 16, 17, 24, 25, 29, 30, 36,
37, 64, 112
],
101:
[64, 4, 37, 36, 7, 8, 14, 15, 16, 17, 112, 22, 24, 25, 26, 29, 30],
102: [
7, 11, 12, 13, 14, 15, 16, 17, 18, 22, 25, 26, 27, 28, 29, 30,
31, 32, 37, 112, 113
],
103: [14, 15, 16, 17, 112, 113, 22, 26, 27, 28, 29, 30, 31],
104: [
69, 71, 112, 113, 114, 16, 20, 21, 22, 23, 24, 25, 26, 27, 28,
30, 31
],
105: [
14, 15, 16, 112, 113, 17, 114, 21, 22, 23, 24, 26, 27, 28, 29,
30, 31
],
106: [112, 113, 114, 16, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31],
107: [0, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 29],
108: [
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 29, 30, 32, 33, 34, 36,
37, 38, 39, 112
],
109: [
32, 33, 34, 35, 36, 37, 38, 39, 42, 43, 12, 13, 14, 16, 17, 18,
19, 20
],
110: [32, 33, 34, 35, 36, 37, 38, 39, 42, 18],
111: [32, 33, 34, 35, 36, 37, 38, 39, 64, 40, 42, 41, 14, 18, 29],
112: [
32, 33, 34, 35, 36, 37, 38, 39, 64, 40, 42, 41, 12, 13, 14, 16,
18, 29
],
113: [32, 33, 34, 35, 36, 37, 38, 39, 42, 43, 14, 17, 18, 19],
114: [
32, 33, 34, 35, 36, 37, 38, 39, 64, 40, 42, 41, 43, 14, 17, 18,
19, 29
],
115: [32, 33, 34, 35, 36, 37, 38, 39, 64, 40, 42, 41, 14, 18, 29],
116:
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 64, 14, 48, 18, 29],
117: [
4, 7, 8, 14, 15, 16, 17, 24, 25, 29, 30, 32, 33, 34, 36, 37,
38, 39, 63, 64, 65, 67, 112
],
118: [1, 2, 3, 4, 5, 6, 7, 10, 11, 13, 15, 116],
119: [32, 33, 34, 36, 37, 38, 39, 40, 41, 48],
120: [32, 33, 34, 36, 37, 38, 39, 40, 41, 48],
121: [
32, 33, 34, 65, 36, 37, 38, 39, 40, 41, 68, 44, 46, 47, 48, 49,
50, 53
],
122: [65, 36, 68, 38, 39, 40, 41, 44, 46, 47, 48, 49, 50, 53],
123: [33, 35, 36, 42, 43, 44, 45, 48, 18],
124: [32, 33, 34, 35, 36, 37, 38, 39, 42, 43, 44, 14, 17, 18, 19],
125: [33, 65, 68, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53],
126: [65, 68, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53],
127:
[65, 67, 68, 69, 41, 74, 43, 44, 45, 46, 47, 48, 49, 50, 76, 53],
128: [65, 68, 41, 43, 44, 45, 46, 47, 48, 49, 50, 53],
129: [2, 3, 4, 5, 37, 7, 8, 9, 10, 16, 25, 29],
130: [
39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 61, 64, 65, 66, 67, 68, 69, 74, 76
],
131: [
36, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54, 55, 56, 57, 58, 61, 64, 65, 66, 67, 68, 69, 74, 76
],
132: [
36, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54, 55, 56, 57, 58, 61, 64, 65, 66, 67, 68, 69, 74, 76
],
133: [
39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 61, 64, 65, 66, 67, 68, 69, 74, 76
],
134: [
39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 61, 64, 65, 66, 67, 68, 69, 74, 76
],
135: [
39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 61, 64, 65, 66, 67, 68, 69, 74, 76
],
136: [
39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 61, 64, 65, 66, 67, 68, 69, 74, 76
],
137: [65, 68, 41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 55, 57],
138: [48, 50, 51, 52, 53, 54, 55, 57, 58],
139: [
41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
60, 62, 65, 68
],
140: [4, 7, 8, 9, 29],
141: [
39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 68, 69, 74, 76
],
142: [
39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 68, 69, 74, 76
],
143: [
41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
60, 62, 65, 68
],
144: [
41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
60, 62, 65, 68
],
145: [48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62],
146: [48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62],
147: [65, 68, 41, 44, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58],
148: [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62],
149: [
65, 68, 41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
58
],
150: [
41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
60, 61, 62, 63, 65, 68
],
151: [1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 116],
152: [48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
153: [48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
154: [48, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
155: [65, 66, 48, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
156:
[64, 65, 66, 48, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
157: [64, 65, 66, 53, 54, 55, 58, 59, 60, 61, 62, 63],
158: [64, 65, 66, 48, 53, 54, 55, 58, 59, 60, 61, 62, 63],
159: [64, 65, 66, 48, 53, 54, 55, 58, 59, 60, 61, 62, 63],
160: [64, 65, 67, 37, 53, 54, 55, 58, 59, 60, 61, 62, 63],
161: [
7, 16, 25, 29, 32, 33, 34, 36, 37, 38, 39, 48, 60, 61, 62, 63,
64, 65, 66, 67, 68, 80, 115
],
162: [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 116, 29],
163: [
64, 65, 66, 67, 36, 37, 68, 48, 80, 61, 115, 58, 59, 60, 29,
62, 63
],
164: [
37, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68, 69, 74, 76
],
165: [
37, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68, 69, 74, 76
],
166: [
37, 41, 44, 46, 47, 48, 49, 50, 53, 58, 59, 60, 61, 63, 64, 65,
66, 67, 68
],
167: [64, 65, 66, 48, 58, 59, 60, 61, 63],
168: [
64, 65, 66, 67, 68, 37, 41, 44, 46, 47, 48, 49, 50, 53, 59, 60,
61, 63
],
169: [
23, 41, 44, 45, 46, 47, 48, 49, 50, 53, 64, 65, 67, 68, 69, 70,
73, 74, 75, 76, 77, 79, 80, 81, 115, 117
],
170: [
23, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 61, 64, 65, 66, 67, 68, 69, 70, 73, 74, 75, 76,
77, 79, 80, 81, 115, 117
],
171: [
22, 23, 41, 44, 45, 46, 47, 48, 49, 50, 53, 64, 65, 67, 68, 69,
70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 115, 117
],
172: [
67, 68, 69, 70, 71, 72, 73, 74, 76, 46, 48, 21, 22, 23, 24,
117, 31
],
173: [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 37, 16, 25, 29],
174: [37, 7, 16, 114, 21, 22, 23, 24, 25, 26, 27, 29, 31],
175: [
64, 67, 68, 74, 75, 76, 77, 78, 79, 80, 81, 93, 94, 95, 96, 97,
98, 99, 115
],
176: [82, 83, 84, 85, 86, 87, 88],
177: [64, 65, 67, 68, 37, 69, 74, 76, 46, 79, 80, 48, 115, 63],
178: [
4, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 24, 25, 28, 29, 30,
31, 32, 36, 37, 64, 112
],
179: [
7, 14, 16, 18, 25, 29, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
42, 63, 64, 65, 67
],
180: [64, 48, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
181: [64, 65, 66, 67, 37, 53, 54, 55, 58, 59, 60, 61, 62, 63],
182: [
29, 36, 37, 41, 44, 46, 47, 48, 49, 50, 53, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 80, 115
],
183: [
29, 36, 37, 46, 48, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 74,
76, 79, 80, 115
],
184: [
23, 37, 41, 44, 45, 46, 47, 48, 49, 50, 53, 63, 64, 65, 67, 68,
69, 70, 73, 74, 75, 76, 77, 79, 80, 81, 115, 117
],
185: [
37, 46, 48, 63, 64, 65, 67, 68, 69, 74, 76, 78, 79, 80, 95, 96,
97, 98, 115
]
}
self.lines = [
2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25,
27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
62, 63, 64, 66, 68, 69, 70, 71, 74, 79, 83, 84, 85, 86, 87, 88, 89,
90, 91, 92, 93, 94, 95, 96, 97, 101, 102, 104, 105, 106, 108, 112,
115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 126, 127, 128,
130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144,
145, 146, 147, 148, 149, 150, 151, 153, 154, 155, 156, 158, 159,
160, 161, 162, 163, 164, 166, 167, 168, 169, 170, 171, 172, 174,
178, 179, 180, 181, 182, 183, 184
]
def __init__(self, *args, **kwargs):
BaseAgent.__init__(self, *args, **kwargs)
def __init__(self, action_space, action_space_directory):
BaseAgent.__init__(self, action_space=action_space)
self.actions62 = np.load(os.path.join(action_space_directory, 'actions62.npy'))
self.actions146 = np.load(os.path.join(action_space_directory, 'actions146.npy'))
def __init__(self, action_space):
BaseAgent.__init__(self, action_space=action_space)
self.simulate_times = 0
unitary_action_model = UnitaryActionModel()
algorithm = ES(unitary_action_model)
self.unitary_es_agent = UnitaryESAgent(algorithm)
combined_actions_model_1 = CombinedActionsModel()
combined_actions_model_2 = CombinedActionsModel()
ensemble_algorithm = EnsembleES(combined_actions_model_1,
combined_actions_model_2)
self.combine_es_agent = CombineESAgent(ensemble_algorithm)
self.unitary_es_agent.restore('./saved_files',
'unitary_action_model.ckpt')
self.combine_es_agent.restore('./saved_files',
'combined_actions_model.ckpt')
unitary_actions_vec = np.load(
"./saved_files/v6_top500_unitary_actions.npz")["actions"]
self.unitary_actions = []
for i in range(unitary_actions_vec.shape[0]):
action = action_space.from_vect(unitary_actions_vec[i])
self.unitary_actions.append(action)
redispatch_actions_vec = np.load(
"./saved_files/redispatch_actions.npz")["actions"]
self.redispatch_actions = []
for i in range(redispatch_actions_vec.shape[0]):
action = action_space.from_vect(redispatch_actions_vec[i])
self.redispatch_actions.append(action)
with open("./saved_files/action_to_sub_id.pickle", "rb") as f:
self.action_to_sub_id = pickle.load(f)
self.after_line56_or_line45_disconnect_actions = []
self.three_sub_action_to_sub_ids = {}
actions_vec = np.load(
"./saved_files/v10_merge_three_sub_actions.npz")["actions"]
for i in range(actions_vec.shape[0]):
action = action_space.from_vect(actions_vec[i])
self.after_line56_or_line45_disconnect_actions.append(action)
with open("saved_files/three_sub_action_to_sub_ids.pickle", "rb") as f:
self.three_sub_action_to_sub_ids = pickle.load(f)
self.used_combine_actions = False
self.redispatch_cnt = 0
self.max_redispatch_cnt = 3
self.serial_actions = []
self.do_nothing_action = action_space({})
self.action_space = action_space
offset = 59
self.action_to_sub_topo = {}
for sub_id, sub_elem_num in enumerate(action_space.sub_info):
self.action_to_sub_topo[sub_id] = (offset, offset + sub_elem_num)
offset += sub_elem_num
self.observation = None
self.redispatch_months = set([3])
