def main(_):
FLAGS.agent = model(params=FLAGS)
FLAGS.environment = get_env(FLAGS)
FLAGS.act = action()
FLAGS.step_max = FLAGS.environment.data_len()
FLAGS.train_freq = 40
FLAGS.update_q_freq = 50
FLAGS.gamma = 0.97
FLAGS.show_log_freq = 5
FLAGS.memory = [] #Experience(FLAGS.memory_size)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
#创建用于保存模型的目录
if not os.path.exists(FLAGS.model_dir):
os.makedirs(FLAGS.model_dir)
start = time.time()
with tf.Session() as sess:
sess.run(init)
eval = evaluation(FLAGS, sess)
ckpt = tf.train.get_checkpoint_state(FLAGS.model_dir)
if ckpt:
print('Loading Model...')
saver.restore(sess, ckpt.model_checkpoint_path)
total_step = 1
print('\t'.join(
map(str, [
"epoch", "epsilon", "total_step", "rewardPerEpoch", "profits",
"lossPerBatch", "elapsed_time"
])))
for epoch in range(FLAGS.epoch_num):
avg_loss_per_batch, total_reward, total_step, profits = run_epch(
FLAGS, sess, total_step)
# total_rewards.append(total_reward)
# total_losses.append(total_loss)
if (epoch + 1) % FLAGS.show_log_freq == 0:
# log_reward = sum(total_rewards[((epoch+1)-FLAGS.show_log_freq):])/FLAGS.show_log_freq
# log_loss = sum(total_losses[((epoch+1)-FLAGS.show_log_freq):])/FLAGS.show_log_freq
elapsed_time = time.time() - start
#print('\t'.join(map(str, [epoch+1, FLAGS.act.epsilon, total_step, log_reward, log_loss, elapsed_time])))
print('\t'.join(
map(str, [
epoch + 1, FLAGS.act.epsilon, total_step, total_reward,
profits, avg_loss_per_batch, elapsed_time
])))
start = time.time()
saver.save(
sess,
FLAGS.model_dir + '\model-' + str(epoch + 1) + '.ckpt')
eval.eval()
def evaluate():
if ARGS.replay == 'PER':
filename = ARGS.results_path + "/" + 'weights_' + str(ARGS.replay) + \
'_' + ARGS.pmethod + '_' + ARGS.env + '_.pt'
else:
filename = ARGS.results_path + "/" + 'weights_' + str(
ARGS.replay) + '_' + ARGS.env + '_.pt'
env, (input_size, output_size) = get_env(ARGS.env)
# set env seed
env.seed(ARGS.seed_value)
network = {
'CartPole-v1':
CartNetwork(input_size, output_size, ARGS.num_hidden).to(device),
'MountainCar-v0':
MountainNetwork(input_size, output_size, ARGS.num_hidden).to(device),
'LunarLander-v2':
LanderNetwork(input_size, output_size, ARGS.num_hidden).to(device)
}
model = network[ARGS.env]
model.eval()
if os.path.isfile(filename):
print(f"Loading weights from {filename}")
# weights = torch.load(filename)
weights = torch.load(filename,
map_location=lambda storage, loc: storage)
model.load_state_dict(weights['policy'])
else:
print("Please train the model or provide the saved 'weights.pt' file")
episode_durations = []
for i in range(20):
state = env.reset()
done = False
steps = 0
while not done:
steps += 1
with torch.no_grad():
state = torch.tensor(state, dtype=torch.float).to(device)
action = get_action(state, model).item()
state, reward, done, _ = env.step(action)
env.render()
episode_durations.append(steps)
print(i)
env.close()
plt.plot(episode_durations)
plt.title('Episode durations')
plt.show()
def main(args):
__log.setLevel(level=getattr(logging, args.log_level))
os.makedirs(args.save_dir, exist_ok=True)
env = get_env(args.env, app_path=args.app_path)
actor = get_actor(args.actor,
input_shapes=[[None, env.state_size]],
output_shapes=[[None, env.action_size]],
load_path=args.ckpt_actor)
if args.train:
Algorithm = get_algorithm(args.algorithm)
algorithm = Algorithm(env=env, actor=actor)
algorithm.run(**vars(args))
if args.eval:
evaluate(env, actor, n_eval_episode=args.n_eval_episode)
if args.play:
show_agent_play(env, actor)
def main():
# update this disctionary as per the implementation of methods
memory = {
'NaiveReplayMemory': NaiveReplayMemory,
'CombinedReplayMemory': CombinedReplayMemory,
'PER': PrioritizedReplayMemory
}
if ARGS.adaptive_buffer:
# Introduces the buffer manager for the adaptive buffer size.
manage_memory = BufferSizeManager(initial_capacity=ARGS.buffer,
size_change=ARGS.buffer_step_size)
# environment
env, (input_size, output_size) = get_env(ARGS.env)
env.seed(ARGS.seed_value)
network = {
'CartPole-v1':
CartNetwork(input_size, output_size, ARGS.num_hidden).to(device),
'MountainCar-v0':
MountainNetwork(input_size, output_size, ARGS.num_hidden).to(device),
'LunarLander-v2':
LanderNetwork(input_size, output_size, ARGS.num_hidden).to(device)
}
# create new file to store durations
i = 0
fd_name = ARGS.results_path + "/" + str(ARGS.buffer) + "_" + str(
ARGS.replay) + "_" + str(
ARGS.pmethod) + '_' + ARGS.env + "_durations0.txt"
exists = os.path.isfile(fd_name)
while exists:
i += 1
fd_name = ARGS.results_path + "/" + str(ARGS.buffer) + "_" + str(
ARGS.replay) + "_" + str(
ARGS.pmethod) + '_' + ARGS.env + "_durations%d.txt" % i
exists = os.path.isfile(fd_name)
fd = open(fd_name, "w+")
# create new file to store rewards
i = 0
fr_name = ARGS.results_path + "/" + str(ARGS.buffer) + "_" + str(
ARGS.replay) + "_" + str(
ARGS.pmethod) + '_' + ARGS.env + "_rewards0.txt"
exists = os.path.isfile(fr_name)
while exists:
i += 1
fr_name = ARGS.results_path + "/" + str(ARGS.buffer) + "_" + str(
ARGS.replay) + "_" + str(
ARGS.pmethod) + '_' + ARGS.env + "_rewards%d.txt" % i
exists = os.path.isfile(fr_name)
fr = open(fr_name, "w+")
# Save experiment hyperparams
i = 0
exists = os.path.isfile(ARGS.results_path + "/" + str(ARGS.buffer) + "_" +
str(ARGS.replay) + "_" + str(ARGS.pmethod) + '_' +
ARGS.env + "_info0.txt")
while exists:
i += 1
exists = os.path.isfile(ARGS.results_path + "/" + str(ARGS.buffer) +
"_" + str(ARGS.replay) + "_" +
str(ARGS.pmethod) + '_' + ARGS.env +
"_info%d.txt" % i)
fi = open(
ARGS.results_path + "/" + str(ARGS.buffer) + "_" + str(ARGS.replay) +
"_" + str(ARGS.pmethod) + '_' + ARGS.env + "_info%d.txt" % i, "w+")
file_counter = i
fi.write(str(ARGS))
fi.close()
# -----------initialization---------------
if ARGS.replay == 'PER':
replay = memory[ARGS.replay](ARGS.buffer, ARGS.pmethod)
filename = ARGS.results_path + "/" + str(
ARGS.buffer
) + "_" + 'weights_' + str(
ARGS.replay
) + '_' + ARGS.pmethod + '_' + ARGS.env + "_%d.pt" % ARGS.seed_value # file_counter # +'_.pt'
else:
replay = memory[ARGS.replay](ARGS.buffer)
filename = ARGS.results_path + "/" + str(
ARGS.buffer
) + "_" + 'weights_' + str(
ARGS.replay
) + '_' + ARGS.env + "_%d.pt" % ARGS.seed_value # file_counter # +'_.pt'
model = network[ARGS.env] # local network
model_target = network[ARGS.env] # target_network
optimizer = optim.Adam(model.parameters(), ARGS.lr)
# Count the steps (do not reset at episode start, to compute epsilon)
global_steps = 0
episode_durations = []
rewards_per_episode = []
buffer_sizes = []
scores_window = deque(maxlen=100)
eps = ARGS.EPS
# -------------------------------------------------------
for i_episode in tqdm(range(ARGS.num_episodes), ncols=50):
# Sample a transition
s = env.reset()
done = False
epi_duration = 0
r_sum = 0
buffer_sizes.append(len(replay))
# for debugging purposes:
if (ARGS.debug_mode):
print(
f"buffer size: {len(replay)}, r: {episode_durations[-1] if len(episode_durations) >= 1 else 0}"
)
render_env_bool = False
if (ARGS.render_env > 0) and not (i_episode % ARGS.render_env):
render_env_bool = True
env.render()
max_steps = 1000
for t in range(max_steps):
# eps = get_epsilon(global_steps) # Comment this to to not use linear decay
model.eval()
a = select_action(model, s, eps)
model.train()
s_next, r, done, _ = env.step(a)
beta = None
# The TD-error is necessary if replay == PER OR if we are using adaptive buffer and the memory is full
get_td_error = (ARGS.replay == 'PER') or (ARGS.adaptive_buffer
and replay.memory_full())
if get_td_error:
state = torch.tensor(s,
dtype=torch.float).to(device).unsqueeze(0)
action = torch.tensor(
a, dtype=torch.int64).to(device).unsqueeze(
0) # Need 64 bit to use them as index
next_state = torch.tensor(
s_next, dtype=torch.float).to(device).unsqueeze(0)
reward = torch.tensor(
r, dtype=torch.float).to(device).unsqueeze(0)
done_ = torch.tensor(done,
dtype=torch.uint8).to(device).unsqueeze(0)
with torch.no_grad():
q_val = compute_q_val(model, state, action)
target = compute_target(model_target, reward, next_state,
done_, ARGS.discount_factor)
td_error = F.smooth_l1_loss(q_val, target)
if ARGS.adaptive_buffer and replay.memory_full():
new_buffer_size = manage_memory.update_memory_size(
td_error.item())
replay.resize_memory(new_buffer_size)
if ARGS.replay == 'PER':
replay.push(abs(td_error), (s, a, r, s_next, done))
beta = get_beta(i_episode, ARGS.num_episodes, ARGS.beta0)
else:
replay.push((s, a, r, s_next, done))
loss = train(model,
model_target,
replay,
optimizer,
ARGS.batch_size,
ARGS.discount_factor,
ARGS.TAU,
global_steps,
beta=beta)
s = s_next
epi_duration += 1
global_steps += 1
if done:
break
r_sum += r
# visualize
if render_env_bool:
env.render()
eps = max(0.01, ARGS.eps_decay * eps)
rewards_per_episode.append(r_sum)
episode_durations.append(epi_duration)
scores_window.append(r_sum)
# store episode data in files
fr.write("%d\n" % r_sum)
fr.close()
fr = open(fr_name, "a")
fd.write("%d\n" % epi_duration)
fd.close()
fd = open(fd_name, "a")
if i_episode % 100 == 0:
print('\rEpisode {}\tAverage Score: {:.2f}'.format(
i_episode, np.mean(scores_window)))
# if np.mean(scores_window)>=200.0:
# print('\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_window)))
# break
# if epi_duration >= 500: # this value is environment dependent
# print("Failed to complete in trial {}".format(i_episode))
# else:
# print("Completed in {} trials".format(i_episode))
# break
# close files
fd.close()
fr.close()
env.close()
# TODO: save all stats in numpy (pickle)
b_name = ARGS.results_path + "/" + str(ARGS.buffer) + "_" + str(
ARGS.replay) + "_" + str(
ARGS.pmethod) + '_' + ARGS.env + "_buffers_sizes_" + str(
ARGS.seed_value)
np.save(b_name, buffer_sizes)
print(f"max episode duration {max(episode_durations)}")
print(f"Saving weights to {filename}")
torch.save(
{
# You can add more here if you need, e.g. critic
'policy':
model.state_dict() # Always save weights rather than objects
},
filename)
plt.plot(smooth(episode_durations, 10))
plt.title('Episode durations per episode')
# plt.show()
plt.savefig(ARGS.images_path + "/" + str(ARGS.buffer) + "_" +
str(ARGS.replay) + '_' + ARGS.pmethod + '_' + ARGS.env +
'_Episode' + "%d.png" % ARGS.seed_value) # file_counter)
plt.plot(smooth(rewards_per_episode, 10))
plt.title("Rewards per episode")
# plt.show()
plt.savefig(ARGS.images_path + "/" + str(ARGS.buffer) + "_" +
str(ARGS.replay) + '_' + ARGS.pmethod + '_' + ARGS.env +
'_Rewards' + "%d.png" % ARGS.seed_value) # file_counter)
return episode_durations
def main():
#update this disctionary as per the implementation of methods
memory = {
'NaiveReplayMemory': NaiveReplayMemory,
'CombinedReplayMemory': CombinedReplayMemory,
'PER': PrioritizedReplayMemory
}
# environment
env, (input_size, output_size) = get_env(ARGS.env)
# env.seed(seed_value)
network = {
'CartPole-v1':
CartNetwork(input_size, output_size, ARGS.num_hidden).to(device),
'MountainCar-v0':
MountainNetwork(input_size, output_size, ARGS.num_hidden).to(device),
'LunarLander-v2':
LanderNetwork(input_size, output_size, ARGS.num_hidden).to(device)
}
# create new file to store durations
i = 0
fd_name = "results/" + str(ARGS.buffer) + "_" + str(
ARGS.replay) + "_" + str(
ARGS.pmethod) + '_' + ARGS.env + "_durations0.txt"
exists = os.path.isfile(fd_name)
while exists:
i += 1
fd_name = "results/" + str(ARGS.buffer) + "_" + str(
ARGS.replay) + "_" + str(
ARGS.pmethod) + '_' + ARGS.env + "_durations%d.txt" % i
exists = os.path.isfile(fd_name)
fd = open(fd_name, "w+")
# create new file to store rewards
i = 0
fr_name = "results/" + str(ARGS.buffer) + "_" + str(
ARGS.replay) + "_" + str(
ARGS.pmethod) + '_' + ARGS.env + "_rewards0.txt"
exists = os.path.isfile(fr_name)
while exists:
i += 1
fr_name = "results/" + str(ARGS.buffer) + "_" + str(
ARGS.replay) + "_" + str(
ARGS.pmethod) + '_' + ARGS.env + "_rewards%d.txt" % i
exists = os.path.isfile(fr_name)
fr = open(fr_name, "w+")
# Save experiment hyperparams
i = 0
exists = os.path.isfile("results/" + str(ARGS.buffer) + "_" +
str(ARGS.replay) + "_" + str(ARGS.pmethod) + '_' +
ARGS.env + "_info0.txt")
while exists:
i += 1
exists = os.path.isfile("results/" + str(ARGS.buffer) + "_" +
str(ARGS.replay) + "_" + str(ARGS.pmethod) +
'_' + ARGS.env + "_info%d.txt" % i)
fi = open(
"results/" + str(ARGS.buffer) + "_" + str(ARGS.replay) + "_" +
str(ARGS.pmethod) + '_' + ARGS.env + "_info%d.txt" % i, "w+")
file_counter = i
fi.write(str(ARGS))
fi.close()
#-----------initialization---------------
if ARGS.replay == 'PER':
replay = memory[ARGS.replay](ARGS.buffer, ARGS.pmethod)
filename = "results/" + str(ARGS.buffer) + "_" + 'weights_' + str(
ARGS.replay
) + '_' + ARGS.pmethod + '_' + ARGS.env + "_%d.pt" % file_counter # +'_.pt'
else:
replay = memory[ARGS.replay](ARGS.buffer)
filename = "results/" + str(ARGS.buffer) + "_" + 'weights_' + str(
ARGS.replay) + '_' + ARGS.env + "_%d.pt" % file_counter #+'_.pt'
model = network[ARGS.env] # local network
model_target = network[ARGS.env] # target_network
optimizer = optim.Adam(model.parameters(), ARGS.lr)
global_steps = 0 # Count the steps (do not reset at episode start, to compute epsilon)
episode_durations = [] #
rewards_per_episode = []
scores_window = deque(maxlen=100)
eps = ARGS.EPS
#-------------------------------------------------------
for i_episode in tqdm(range(ARGS.num_episodes)):
# YOUR CODE HERE
# Sample a transition
s = env.reset()
done = False
epi_duration = 0
r_sum = 0
for t in range(1000):
# eps = get_epsilon(global_steps) # Comment this to to not use linear decay
model.eval()
a = select_action(model, s, eps)
model.train()
s_next, r, done, _ = env.step(a)
beta = None
if ARGS.replay == 'PER':
state = torch.tensor(s,
dtype=torch.float).to(device).unsqueeze(0)
action = torch.tensor(
a, dtype=torch.int64).to(device).unsqueeze(
0) # Need 64 bit to use them as index
next_state = torch.tensor(
s_next, dtype=torch.float).to(device).unsqueeze(0)
reward = torch.tensor(
r, dtype=torch.float).to(device).unsqueeze(0)
done_ = torch.tensor(done,
dtype=torch.uint8).to(device).unsqueeze(0)
with torch.no_grad():
q_val = compute_q_val(model, state, action)
target = compute_target(model_target, reward, next_state,
done_, ARGS.discount_factor)
td_error = F.smooth_l1_loss(q_val, target)
replay.push(td_error, (s, a, r, s_next, done))
beta = get_beta(i_episode, ARGS.num_episodes, ARGS.beta0)
else:
replay.push((s, a, r, s_next, done))
loss = train(model,
model_target,
replay,
optimizer,
ARGS.batch_size,
ARGS.discount_factor,
ARGS.TAU,
global_steps,
beta=beta)
s = s_next
epi_duration += 1
global_steps += 1
if done:
break
r_sum += r
#visualize
# env.render()
eps = max(0.01, ARGS.eps_decay * eps)
rewards_per_episode.append(r_sum)
episode_durations.append(epi_duration)
scores_window.append(r_sum)
# store episode data in files
fr.write("%d\n" % r_sum)
fr.close()
fr = open(fr_name, "a")
fd.write("%d\n" % epi_duration)
fd.close()
fd = open(fd_name, "a")
if i_episode % 100 == 0:
print('\rEpisode {}\tAverage Score: {:.2f}'.format(
i_episode, np.mean(scores_window)))
# if np.mean(scores_window)>=200.0:
# print('\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_window)))
# break
# if epi_duration >= 500: # this value is environment dependent
# print("Failed to complete in trial {}".format(i_episode))
# else:
# print("Completed in {} trials".format(i_episode))
# break
# close files
fd.close()
fr.close()
env.close()
print(f"Saving weights to {filename}")
torch.save(
{
# You can add more here if you need, e.g. critic
'policy':
model.state_dict() # Always save weights rather than objects
},
filename)
plt.plot(smooth(episode_durations, 10))
plt.title('Episode durations per episode')
#plt.show()
plt.savefig("images/" + str(ARGS.buffer) + "_" + str(ARGS.replay) + '_' +
ARGS.pmethod + '_' + ARGS.env + '_Episode' +
"%d.png" % file_counter)
plt.plot(smooth(rewards_per_episode, 10))
plt.title("Rewards per episode")
#plt.show()
plt.savefig("images/" + str(ARGS.buffer) + "_" + str(ARGS.replay) + '_' +
ARGS.pmethod + '_' + ARGS.env + '_Rewards' +
"%d.png" % file_counter)
return episode_durations
def sample(self, n):
return self.container.get_batch(n)
def update(self, idx, error):
self.container.update(idx, error)
def __len__(self):
return self.container.get_len()
#sanity check
if __name__=="__main__":
capacity = 10
memory = PrioritizedReplayMemory(capacity)#CombinedReplayMemory(capacity)#NaiveReplayMemory(capacity)
env, _ = get_env("Acrobot-v1")
# Sample a transition
s = env.reset()
a = env.action_space.sample()
s_next, r, done, _ = env.step(a)
# Push a transition
err = 0.5
memory.push(err,(s, a, r, s_next, done))
# Sample a batch size of 1
print(memory.sample(1))
from collections import OrderedDict
import json
import sys
from environment import get_env
Lock = get_env("Lock")
class EOF(Exception):
pass
class InvalidInput(Exception):
pass
# TODO error handling
class Connection(object):
def __init__(self, read, write):
self._read = read
self._write = write
self._write_lock = Lock()
def serve(self, once=False):
state = 0
while not self._read.closed:
if state == 0:
headers = {}
state = 1
def __init__(self, params, sess):
self.env = get_env(params, tf.estimator.ModeKeys.EVAL)
self.params = params
self.sess = sess
def forward(self, state):
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
return self.fc3(x)
#TODO: implement a network based on obeservation. Also use CNNs
if __name__=="__main__":
# Let's instantiate and test if it works
num_hidden = 128
torch.manual_seed(1234)
env, _size = get_env("Acrobot-v1")
input_size, output_size = _size
# Sample a transition
s = env.reset()
a = env.action_space.sample()
s_next, r, done, _ = env.step(a)
model = QNetwork(input_size, output_size, num_hidden)
torch.manual_seed(1234)
test_model = nn.Sequential(
nn.Linear(input_size, num_hidden),
nn.ReLU(),
nn.Linear(num_hidden, output_size)
)
from collections import deque
import json
import os
import time
from connection import Resolver, EOF
from environment import get_env
Lock, sleep, spawn, subprocess, Popen = get_env(
"Lock", "sleep", "spawn", "subprocess", "Popen"
)
class CannotRunError(Exception):
def __init__(self, message, originalException=None):
self.originalException = originalException
super(CannotRunError, self).__init__(message)
# The requests implementation is more complex here if you don't want to send
# a different request per plugin based on when you last got results from it.
class PluginBridge(object):
def __init__(self, name, metadata_path):
self._name = name
self._metadata_dir = os.path.dirname(os.path.abspath(metadata_path))
with open(metadata_path, "rb") as f:
self._metadata = json.load(f)
self._proc = None
self._resolver = False
self._requests = deque()
self._requests_lock = Lock()
self._request_start_time = None
lams = np.zeros(shape = (len(demand_points_list), time_horizon)) # arrival rates for hospitalisation for each demand point for different
for i in range(len(demand_points_list)):
lams[i] = np.random.normal(hospitalisation_mean[i], hospitalisation_sd[i], time_horizon)
burn_rate_mean = [6, 6, 6, 6, 6] # burn rate mean (burn rate is calculated per day per patient)
burn_rate_sd = [2, 2, 2, 2, 2] # burn rate sd
death_rate = 0.04 # Proportion of people hospitalised that die
mean_LOS_death = 7 # Avergae LOS (Length of Stay) for patients that die (in days)
mean_LOS_discharge = 10 # Average LOS for patients that get discarhegd (in days)
avg_interarrival_time_supply = 7 # On an average weekly interarrival between supplies
supply_amount = 10000
vehicles = 100
# Rendering the environement
env = get_env(demand_points_list, vehicles, time_horizon)
env.set(initial_inv = initial_inv , lams = lams, hospitalisation_trend = hospitalisation_trend,
burn_rate_mean = burn_rate_mean, burn_rate_sd = burn_rate_sd, death_rate = death_rate , mean_LOS_death = mean_LOS_death,
mean_LOS_discharge = mean_LOS_discharge, average_interarrival_time = avg_interarrival_time_supply, supply_amount = supply_amount)
# Simulating states/state variables if no action is taken
env.actionlesss_simulation()
## Plotting the results of the simulation
# Plotting hospitalisations, deaths, discharges and active cases for each demand point by day
plot.plot_cases(time_horizon, env, demand_points_list)
# Plotting PPE inventory for each demand point by day
