It is recommended to have a look at the 0_basic_functionalities, 1_Observation_Agents and 2_Action_GridManipulation and especially 3_TrainingAnAgent notebooks before getting into this one.
Objectives
In this notebook we will expose :
res = None
try:
from jyquickhelper import add_notebook_menu
res = add_notebook_menu()
except ModuleNotFoundError:
print("Impossible to automatically add a menu / table of content to this notebook.\nYou can download \"jyquickhelper\" package with: \n\"pip install jyquickhelper\"")
res
import grid2op
from grid2op.Reward import ConstantReward, FlatReward
from tqdm.notebook import tqdm
from grid2op.Runner import Runner
import sys
import os
import numpy as np
TRAINING_STEP = 100
By default we use the test environment. But by passing test=False
in the following function will automatically download approximately 300MB from the internet and give you 1000 chronics instead of 2 used for this example.
env = grid2op.make("rte_case14_realistic", test=True)
/home/benjamin/Documents/grid2op_dev/getting_started/grid2op/MakeEnv/Make.py:240: UserWarning: You are using a development environment. This environment is not intended for training agents.
A lot of data have been made available for the default "rte_case14_realistic" environment. Including this data in the package is not convenient.
We chose instead to release them and make them easily available with a utility. To download them in the default directory ("~/data_grid2op/case14_redisp") just pass the argument "test=False" (or don't pass anything else) as local=False is the default value. It will download approximately 300Mo of data.
Make sure you are using a computer with at least 4 cores if you want to notice some speed-ups.
from grid2op.Environment import MultiEnvironment
from grid2op.Agent import DoNothingAgent
NUM_CORE = 8
Here we demonstrate how to use the multi environment class. First let's create a multi environment.
# create a simple agent
agent = DoNothingAgent(env.action_space)
# create the multi environment class
multi_envs = MultiEnvironment(env=env, nb_env=NUM_CORE)
A multienvironment is just like a regular environment but instead of dealing with one action, and one observation, is requires to be sent multiple actions, and returns a list of observations as well.
It requires a grid2op environment to be initialized and creates some specific "workers", each a replication of the initial environment. None of the "worker" can be accessed directly. Supported methods are:
That have similar behaviour to "env.step", "env.close" or "env.reset".
It can be used the following manner.
# initiliaze some variable with the proper dimension
obss = multi_envs.reset()
rews = [env.reward_range[0] for i in range(NUM_CORE)]
dones = [False for i in range(NUM_CORE)]
obss
array([<grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eaae79d0>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eaae7a30>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eaae7a90>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eaae7af0>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eaae7b50>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eaae7bb0>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eaae7be0>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eaae7c70>], dtype=object)
dones
[False, False, False, False, False, False, False, False]
As you can see, obs is not a single obervation, but a list (numpy nd array to be precise) of 4 observations, each one being an observation of a given "worker" environment.
Worker environments are always called in the same order. It means the first observation of this vector will always correspond to the first worker environment.
Similarly to Observation, the "step" function of a multi_environment takes as input a list of multiple actions, each action will be implemented in its own environment. It returns a list of observations, a list of rewards, and boolean list of whether or not the worker environment suffer from a game over (in that case this worker environment is automatically restarted using the "reset" method.)
Because orker environments are always called in the same order, the first action sent to the "multi_env.step" function will also be applied on this first environment.
It is possible to use it as follow:
# initialize the vector of actions that will be processed by each worker environment.
acts = [None for _ in range(NUM_CORE)]
for env_act_id in range(NUM_CORE):
acts[env_act_id] = agent.act(obss[env_act_id], rews[env_act_id], dones[env_act_id])
# feed them to the multi_env
obss, rews, dones, infos = multi_envs.step(acts)
# as explained, this is a vector of Observation (as many as NUM_CORE in this example)
obss
array([<grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd4344025e0>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd4344027f0>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3ef6921c0>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd434470970>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eab11070>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eab110d0>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eab11130>, <grid2op.Space.GridObjects.CompleteObservation_rte_case14_realistic object at 0x7fd3eab11190>], dtype=object)
The multi environment loop is really close to the "gym" loop:
# performs the appropriated steps
for i in range(10):
acts = [None for _ in range(NUM_CORE)]
for env_act_id in range(NUM_CORE):
acts[env_act_id] = agent.act(obss[env_act_id], rews[env_act_id], dones[env_act_id])
obss, rews, dones, infos = multi_envs.step(acts)
# DO SOMETHING WITH THE AGENT IF YOU WANT
## agent.train(obss, rews, dones)
# close the environments created by the multi_env
multi_envs.close()
On the above example, TRAINING_STEP
steps are performed on NUM_CORE
environments in parrallel. The agent has then acted TRAINING_STEP * NUM_CORE
(=10 * 4 = 40
by default) times on NUM_CORE
different environments.
We reuse the code of the Notebook 3_TrainingAnAgent to train a new agent, but this time using more than one process of the machine. To further emphasize the working of multi environments, we put on a different module (ml_agent) the code of some agents and focus here on the training part.
Note that compare to the previous notebook, the code have been adapted to used "batch" of data when predicting movments. The input data is also restricted to:
All the other component of the observations are not used.
from ml_agent import TrainingParam, ReplayBuffer, DeepQAgent
from grid2op.Agent import AgentWithConverter
from grid2op.Reward import RedispReward
from grid2op.Converter import IdToAct
import numpy as np
import random
import warnings
import pdb
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
import tensorflow.keras
import tensorflow.keras.backend as K
from tensorflow.keras.models import load_model, Sequential, Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense, subtract, add
from tensorflow.keras.layers import Input, Lambda, Concatenate
class TrainAgentMultiEnv(object):
def __init__(self, agent, nb_process, reward_fun=RedispReward, env=None, name=None):
# compare to the version showed in the notebook 3, the process buffer has been moved in this class
# and we add a multi_envs argument.
self.nb_process = nb_process
self.multi_envs = None
self.process_buffer = [[] for _ in range(self.nb_process)]
self.name = name
self.agent = agent
self.env = env
self.training_param = None
def close(self):
self.multi_envs.close()
def convert_process_buffer(self):
"""Converts the list of NUM_FRAMES images in the process buffer
into one training sample"""
# here i simply concatenate the action in case of multiple action in the "buffer"
if self.training_param.NUM_FRAMES != 1:
raise RuntimeError("This has not been tested with self.training_param.NUM_FRAMES != 1 for now")
return np.array([np.concatenate(el) for el in self.process_buffer])
def _build_valid_env(self, training_param):
# this function has also be adapted
create_new = False
if self.multi_envs is None:
create_new = True
# first we need to initialize the multi environment
self.multi_envs = MultiEnvironment(env=env, nb_env=self.nb_process)
# then, as before, we reset it
obss = self.multi_envs.reset()
for worker_id in range(self.nb_process):
self.process_buffer[worker_id].append(self.agent.convert_obs(obss[worker_id]))
# used in case of "num frames" != 1 (so not tested)
do_nothing = [self.env.action_space() for _ in range(self.nb_process)]
for _ in range(training_param.NUM_FRAMES-1):
# Initialize buffer with the first frames
s1, r1, _, _ = self.multi_envs.step(do_nothing)
for worker_id in range(self.nb_process):
# difference compared to previous implementation: we loop through all the observations
# and save them all
self.process_buffer[worker_id].append(self.agent.convert_obs(s1[worker_id]))
return create_new
def train(self, num_frames, training_param=TrainingParam()):
self.training_param = training_param
# first we create an environment or make sure the given environment is valid
close_env = self._build_valid_env(training_param)
# same as in the original implemenation, except the process buffer is now in this class
observation_num = 0
curr_state = self.convert_process_buffer()
# we initialize the NN exactly as before
self.agent.init_deep_q(curr_state)
# some parameters have been move to a class named "training_param" for convenience
epsilon = training_param.INITIAL_EPSILON
# now the number of alive frames and total reward depends on the "underlying environment". It is vector instead
# of scalar
alive_frame = np.zeros(self.nb_process, dtype=np.int)
total_reward = np.zeros(self.nb_process, dtype=np.float)
with tqdm(total=num_frames) as pbar:
while observation_num < num_frames:
if observation_num % 1000 == 999:
print(("Executing loop %d" %observation_num))
# for efficient reading of data: at early stage of training, it is advised to load
# data by chunk: the model will do game over pretty easily (no need to load all the dataset)
tmp = min(10000 * (num_frames // observation_num), 10000)
self.multi_envs.set_chunk_size(int(max(100, tmp)))
# Slowly decay the learning rate
if epsilon > training_param.FINAL_EPSILON:
epsilon -= (training_param.INITIAL_EPSILON-training_param.FINAL_EPSILON)/training_param.EPSILON_DECAY
initial_state = self.convert_process_buffer()
self.process_buffer = [[] for _ in range(self.nb_process)]
# TODO vectorize that in the Agent directly
# then we need to predict the next moves. Agents have been adapted to predict a batch of data
pm_i, pq_v = self.agent.deep_q.predict_movement(curr_state, epsilon)
# and build the convenient vectors (it was scalars before)
predict_movement_int = []
predict_q_value = []
acts = []
for p_id in range(self.nb_process):
predict_movement_int.append(pm_i[p_id])
predict_q_value.append(pq_v[p_id])
# and then we convert it to a valid action
acts.append(self.agent.convert_act(pm_i[p_id]))
# same loop as in notebook 3
reward, done = np.zeros(self.nb_process), np.full(self.nb_process, fill_value=False, dtype=np.bool)
for i in range(training_param.NUM_FRAMES):
temp_observation_obj, temp_reward, temp_done, _ = self.multi_envs.step(acts)
# we need to handle vectors for "done"
reward[~temp_done] += temp_reward[~temp_done]
# and then "de stack" the observations coming from different environments
for worker_id, obs in enumerate(temp_observation_obj):
self.process_buffer[worker_id].append(self.agent.convert_obs(temp_observation_obj[worker_id]))
done = done | temp_done
# increase of 1 the number of frame alive for relevant "underlying environments"
alive_frame[~temp_done] += 1
# loop through the environment where a game over was done, and print the results
for env_done_idx in np.where(temp_done)[0]:
print("For env with id {}".format(env_done_idx))
print("\tLived with maximum time ", alive_frame[env_done_idx])
print("\tEarned a total of reward equal to ", total_reward[env_done_idx])
reward[temp_done] = 0.
total_reward[temp_done] = 0.
total_reward += reward
alive_frame[temp_done] = 0
# vectorized version of the previous code
new_state = self.convert_process_buffer()
# same as before, but looping through the "underlying environment"
for sub_env_id in range(self.nb_process):
self.agent.replay_buffer.add(initial_state[sub_env_id],
predict_movement_int[sub_env_id],
reward[sub_env_id],
done[sub_env_id],
new_state[sub_env_id])
if self.agent.replay_buffer.size() > training_param.MIN_OBSERVATION:
s_batch, a_batch, r_batch, d_batch, s2_batch = self.agent.replay_buffer.sample(training_param.MINIBATCH_SIZE)
isfinite = self.agent.deep_q.train(s_batch, a_batch, r_batch, d_batch, s2_batch, observation_num)
self.agent.deep_q.target_train()
if not isfinite:
# if the loss is not finite i stop the learning
print("ERROR INFINITE LOSS")
break
# Save the network every 10000 iterations
if observation_num % 10000 == 9999 or observation_num == num_frames-1:
print("Saving Network")
if self.name is None:
self.agent.deep_q.save_network("saved_notebook6.h5")
else:
self.agent.deep_q.save_network("saved_notebook6_{}".format(self.name))
observation_num += 1
pbar.update(1)
if close_env:
print("closing env")
self.env.close()
We redifine the class used to train the agent.
agent_name = "sac_1e5"
my_agent = DeepQAgent(env.action_space, mode="SAC", training_param=TrainingParam())
trainer = TrainAgentMultiEnv(agent=my_agent, env=env, nb_process=NUM_CORE, name=agent_name)
# trainer = TrainAgent(agent=my_agent, env=env)
trainer.train(TRAINING_STEP)
trainer.close()
Successfully constructed networks.
HBox(children=(FloatProgress(value=0.0), HTML(value='')))
For env with id 7 Lived with maximum time 1 Earned a total of reward equal to 1097.7802734375 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 1 Lived with maximum time 2 Earned a total of reward equal to 1080.654296875 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2185.778564453125 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 3 Lived with maximum time 4 Earned a total of reward equal to -40.0 For env with id 2 Lived with maximum time 1 Earned a total of reward equal to 1097.7802734375 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.0509033203125 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 3 Earned a total of reward equal to 3282.8160400390625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 6 Lived with maximum time 5 Earned a total of reward equal to 5473.764892578125 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.4998779296875 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 3 Earned a total of reward equal to 2175.793701171875 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 1 Earned a total of reward equal to 1097.7802734375 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.3878173828125 For env with id 3 Lived with maximum time 9 Earned a total of reward equal to 1012.5396728515625 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 3 Lived with maximum time 6 Earned a total of reward equal to 1050.5550537109375 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.421142578125 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.0509033203125 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 5 Lived with maximum time 26 Earned a total of reward equal to 3265.4735107421875 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 1 Earned a total of reward equal to 1089.6983642578125 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2186.3778076171875 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 1 Lived with maximum time 40 Earned a total of reward equal to 3106.0843505859375 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 5 Lived with maximum time 20 Earned a total of reward equal to 3236.4779052734375 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 1 Lived with maximum time 4 Earned a total of reward equal to 1062.0 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 1 Earned a total of reward equal to 1090.659423828125 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 4 Earned a total of reward equal to 4375.594482421875 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 3 Earned a total of reward equal to 3283.516357421875 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 5 Earned a total of reward equal to 5474.284912109375 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2186.7286376953125 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 3 Earned a total of reward equal to 3283.5400390625 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 3 Lived with maximum time 40 Earned a total of reward equal to 3037.9542236328125 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 1 Earned a total of reward equal to 1090.447021484375 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 3 Earned a total of reward equal to 3281.5516357421875 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.56103515625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 1 Earned a total of reward equal to 1097.7802734375 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 1 Earned a total of reward equal to -10.0 For env with id 7 Lived with maximum time 1 Earned a total of reward equal to 1090.588623046875 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 5 Lived with maximum time 33 Earned a total of reward equal to 3120.75927734375 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 3 Lived with maximum time 21 Earned a total of reward equal to 959.4993896484375 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2159.9930419921875 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 1 Earned a total of reward equal to 1090.693115234375 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.56103515625 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 5 Lived with maximum time 10 Earned a total of reward equal to 2166.345703125 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 3 Lived with maximum time 8 Earned a total of reward equal to 1041.439208984375 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 5 Earned a total of reward equal to 5368.266845703125 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 6 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 5 Lived with maximum time 4 Earned a total of reward equal to 1061.8560791015625 For env with id 6 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 7 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 0 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 2 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 2 Earned a total of reward equal to 2187.5640869140625 For env with id 7 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 0 Lived with maximum time 0 Earned a total of reward equal to 0.0 For env with id 4 Lived with maximum time 0 Earned a total of reward equal to 0.0 Saving Network Successfully saved network. closing env
import matplotlib.pyplot as plt
%matplotlib inline
plt.figure(figsize=(30,20))
plt.plot(my_agent.deep_q.qvalue_evolution)
plt.axhline(y=0, linewidth=3, color='red')
_ = plt.xlim(0, len(my_agent.deep_q.qvalue_evolution))
A loss trained for 100000 iterations on 8 cores, for a ddqn agent and default parameters can look like:
First we evaluate the performance of a baseline, in this case the "do nothing" agent.
NB The use of a Runner (see the first notebook) is particurlaly suited for that purpose. We are showing here how to quickly assess the performances.
NB_EPISODE = 2
max_iter = 100
# tun the do nothing for the whole episode
dn_agent = grid2op.Agent.DoNothingAgent(env.action_space)
runner = Runner(**env.get_params_for_runner(), agentInstance=dn_agent, agentClass=None)
res = runner.run(nb_episode=NB_EPISODE, max_iter=max_iter, pbar=tqdm)
print("The results for the DoNothing agent are:")
for _, chron_id, cum_reward, nb_time_step, max_ts in res:
msg_tmp = "\tFor chronics with id {}\n".format(chron_id)
msg_tmp += "\t\t - cumulative reward: {:.6f}\n".format(cum_reward)
msg_tmp += "\t\t - number of time steps completed: {:.0f} / {:.0f}".format(nb_time_step, max_ts)
print(msg_tmp)
HBox(children=(FloatProgress(value=0.0, description='episode', max=2.0, style=ProgressStyle(description_width=…
HBox(children=(FloatProgress(value=1.0, bar_style='info', description='episode', max=1.0, style=ProgressStyle(…
HBox(children=(FloatProgress(value=1.0, bar_style='info', description='episode', max=1.0, style=ProgressStyle(…
The results for the DoNothing agent are: For chronics with id 000 - cumulative reward: 122885.140625 - number of time steps completed: 100 / 100 For chronics with id 001 - cumulative reward: 122958.148438 - number of time steps completed: 100 / 100
Then we load the saved neural network, and we can now evaluate the fixed policy:
obs = env.reset()
trained_agent = DeepQAgent(env.action_space, mode="DDQN", training_param=TrainingParam())
trained_agent.init_deep_q(trained_agent.convert_obs(obs))
trained_agent.load_network("saved_notebook6_{}".format(agent_name))
runner = Runner(**env.get_params_for_runner(),
agentInstance=trained_agent, agentClass=None)
res = runner.run(nb_episode=NB_EPISODE,
max_iter=max_iter, pbar=tqdm)
print("The results for the DoNothing agent are:")
for _, chron_id, cum_reward, nb_time_step, max_ts in res:
msg_tmp = "\tFor chronics with id {}\n".format(chron_id)
msg_tmp += "\t\t - cumulative reward: {:.6f}\n".format(cum_reward)
msg_tmp += "\t\t - number of time steps completed: {:.0f} / {:.0f}".format(nb_time_step, max_ts)
print(msg_tmp)
Successfully constructed networks. Succesfully loaded network.
HBox(children=(FloatProgress(value=0.0, description='episode', max=2.0, style=ProgressStyle(description_width=…
HBox(children=(FloatProgress(value=1.0, bar_style='info', description='episode', max=1.0, style=ProgressStyle(…
HBox(children=(FloatProgress(value=1.0, bar_style='info', description='episode', max=1.0, style=ProgressStyle(…
The results for the DoNothing agent are: For chronics with id 000 - cumulative reward: 122885.140625 - number of time steps completed: 100 / 100 For chronics with id 001 - cumulative reward: 122958.148438 - number of time steps completed: 100 / 100
A default DDQN agent trained on 8 cores on 1000000 steps (so 8e6 steps in total), 24h of training on a laptop achieved to perform 5637 steps, largely outperforming the "do nothing" agent (which did only 2180 steps on the same 2 environment).