def train(self, obs_n, act_n, adjacency):
with tf.GradientTape() as tape:
# linear output layer
x = self.forward_pass(obs_n[self.agent_index])
act_n = tf.unstack(act_n)
if self.use_gumbel:
logits = x
# log probabilities of the gumbel softmax dist are the output of the network
act_n[self.agent_index] = self.gumbel_softmax_sample(logits)
elif self.use_ounoise:
act_n[self.
agent_index] = x + self.noise * self.noise_mode.noise()
act_n[self.agent_index] = tf.clip_by_value(
act_n[self.agent_index], -1, 1)
else:
act_n[self.agent_index] = x
# q_value = self.q_network._predict_internal(obs_n + act_n)
concatenated_input = tf.concat([obs_n, act_n], axis=-1)
concatenated_input = tf.transpose(concatenated_input, [1, 0, 2])
q_value = self.q_network.model([concatenated_input, adjacency])
# policy_regularization = tf.math.reduce_mean(tf.math.square(x))
policy_regularization = tf.math.reduce_mean(x)
loss = -tf.math.reduce_mean(
q_value
) + 1e-3 * policy_regularization # gradient plus regularization
gradients = tape.gradient(loss, self.model.trainable_variables
) # todo not sure if this really works
# gradients = tf.clip_by_global_norm(gradients, self.clip_norm)[0]
local_clipped = clip_by_local_norm(gradients, self.clip_norm)
self.optimizer.apply_gradients(
zip(local_clipped, self.model.trainable_variables))
return loss
def train(self, obs_n, act_n, q_network):
with tf.GradientTape() as tape:
# linear output layer
x = self.forward_pass(obs_n[self.agent_index])
act_n = tf.unstack(act_n)
if self.use_gumbel:
logits = x
# log probabilities of the gumbel softmax dist are the output of the network
act_n[self.agent_index] = self.gumbel_softmax_sample(logits)
elif self.use_ounoise:
act_n[self.agent_index] += self.noise * self.noise_mode.noise(
) # For continuous actions
act_n[self.agent_index] = tf.clip_by_value(
act_n[self.agent_index], -1, 1)
else:
act_n[self.agent_index] = x
q_value = q_network.predict(obs_n, act_n)
# policy_regularization = tf.math.reduce_mean(tf.math.square(x))
policy_regularization = tf.math.reduce_mean(x)
loss = -tf.math.reduce_mean(
q_value
) + 1e-3 * policy_regularization # gradient plus regularization
gradients = tape.gradient(loss, self.model.trainable_variables)
# gradients = tf.clip_by_global_norm(gradients, self.clip_norm)[0]
local_clipped = u.clip_by_local_norm(gradients, self.clip_norm)
self.optimizer.apply_gradients(
zip(local_clipped, self.model.trainable_variables))
return loss
def _train_step_internal(self, concatenated_input, adjacency, target_q):
"""
Internal function, because concatenation can not be done inside tf.function
"""
with tf.GradientTape() as tape:
q_pred = self.model([concatenated_input, adjacency], training=True)
td_loss = tf.math.square(target_q - q_pred)
loss = tf.reduce_mean(td_loss)
gradients = tape.gradient(loss, self.model.trainable_variables)
local_clipped = clip_by_local_norm(gradients, self.clip_norm)
self.optimizer.apply_gradients(
zip(local_clipped, self.model.trainable_variables))
#
# with tf.GradientTape() as tape:
# x = self.input_concat_layer(concatenated_input)
# for idx in range(self.num_layers):
# x = self.hidden_layers[idx](x)
# q_pred = self.output_layer(x)
# td_loss = tf.math.square(target_q - q_pred)
# loss = tf.reduce_mean(td_loss * weights)
#
# gradients = tape.gradient(loss, self.model.trainable_variables)
#
# local_clipped = clip_by_local_norm(gradients, self.clip_norm)
# self.optimizer.apply_gradients(zip(local_clipped, self.model.trainable_variables))
return td_loss
def _train_step_internal(self, concatenated_input, adjacency, target_q):
"""
Internal function, because concatenation can not be done inside tf.function
"""
with tf.GradientTape() as tape:
q_pred = self.model([concatenated_input, adjacency], training=True)
td_loss = tf.math.square(target_q - q_pred)
loss = tf.reduce_mean(td_loss)
gradients = tape.gradient(loss, self.model.trainable_variables)
local_clipped = clip_by_local_norm(gradients, self.clip_norm)
self.optimizer.apply_gradients(
zip(local_clipped, self.model.trainable_variables))
return td_loss
def _train_step_internal(self, concatenated_input, target_q):
"""
Internal function, because concatenation can not be done inside tf.function
"""
with tf.GradientTape() as tape:
x = self.input_concat_layer(concatenated_input)
for idx in range(self.no_layers):
x = self.hidden_layers[idx](x)
q_pred = self.output_layer(x)
td_loss = tf.math.square(target_q - q_pred)
loss = tf.reduce_mean(td_loss)
gradients = tape.gradient(loss, self.model.trainable_variables)
local_clipped = u.clip_by_local_norm(gradients, self.clip_norm)
self.optimizer.apply_gradients(
zip(local_clipped, self.model.trainable_variables))
return loss, td_loss
def train(self, obs_n, act_n):
with tf.GradientTape() as tape:
# linear output layer
x = self.forward_pass(obs_n[self.agent_index])
act_n = tf.unstack(act_n)
if self.use_gumbel:
logits = x
# log probabilities of the gumbel softmax dist are the output of the network
act_n[self.agent_index] = self.gumbel_softmax_sample(logits)
else:
act_n[self.agent_index] = x
q_value = self.q_network._predict_internal(obs_n + act_n)
policy_regularization = tf.math.reduce_mean(tf.math.square(x))
loss = -tf.math.reduce_mean(
q_value
) + 1e-3 * policy_regularization # gradient plus regularization
gradients = tape.gradient(loss, self.model.trainable_variables
) # todo not sure if this really works
# gradients = tf.clip_by_global_norm(gradients, self.clip_norm)[0]
local_clipped = clip_by_local_norm(gradients, self.clip_norm)
self.optimizer.apply_gradients(
zip(local_clipped, self.model.trainable_variables))
return loss
def main(arglist):
global no_actions, no_features, no_agents
env = u.make_env(arglist.scenario, arglist.no_agents)
obs_shape_n = env.observation_space
act_shape_n = env.action_space
act_shape_n = u.space_n_to_shape_n(act_shape_n)
no_agents = env.n
batch_size = arglist.batch_size
no_neighbors = arglist.no_neighbors
k_lst = list(range(no_neighbors + 2))[2:] # [2,3]
u.create_seed(arglist.seed)
noise_mode = OUNoise(act_shape_n[0], scale=1.0)
noise = 0.1
reduction_noise = 0.999
# Velocity.x Velocity.y Pos.x Pos.y {Land.Pos.x Land.Pos.y}*10 {Ent.Pos.x Ent.Pos.y}*9
no_features = obs_shape_n[0].shape[0]
no_actions = act_shape_n[0][0]
model, model_t = __build_conf()
optimizer = AdamW(learning_rate=arglist.lr, weight_decay=1e-5)
# Results
episode_rewards = [0.0] # sum of rewards for all agents
result_path = os.path.join("results", arglist.exp_name)
res = os.path.join(result_path, " %s.csv" % arglist.exp_name)
if not os.path.exists(result_path):
os.makedirs(result_path)
replay_buffer = ReplayBuffer(arglist.max_buffer_size) # Init Buffer
episode_step = 0
train_step = 0
t_start = time.time()
obs_n = env.reset()
adj = u.get_adj(obs_n, k_lst, no_agents, is_gcn=True)
print('Starting iterations...')
while True:
episode_step += 1
terminal = (episode_step >= arglist.max_episode_len)
if episode_step % 3 == 0:
adj = u.get_adj(obs_n, k_lst, no_agents, is_gcn=True)
predictions = get_predictions(u.to_tensor(np.array(obs_n)), adj, model)
actions = get_actions(predictions, noise, noise_mode)
# Observe next state, reward and done value
new_obs_n, rew_n, done_n, _ = env.step(actions)
done = all(done_n) or terminal
cooperative_reward = rew_n[0]
# Store the data in the replay memory
replay_buffer.add(obs_n, adj, actions, cooperative_reward, new_obs_n,
done)
obs_n = new_obs_n
episode_rewards[-1] += cooperative_reward
if done or terminal:
obs_n = env.reset()
episode_step = 0
episode_rewards.append(0)
# increment global step counter
train_step += 1
# for displaying learned policies
if arglist.display:
time.sleep(0.1)
env.render()
continue
# Train the models
train_cond = not arglist.display
if train_cond and len(replay_buffer) > arglist.batch_size:
if len(
episode_rewards
) % arglist.update_rate == 0: # only update every 30 episodes
for _ in range(arglist.update_times):
state, adj_n, actions, rewards, new_state, dones = replay_buffer.sample(
batch_size)
noise *= reduction_noise
# Calculate TD-target
with tf.GradientTape() as tape:
target_q_values = model_t([new_state, adj_n])
# Apply max(Q) to obtain the TD-target
target_q_tot = tf.reduce_max(target_q_values, axis=-1)
# Apply VDN to reduce the agent-dimension
max_q_tot = tf.reduce_sum(target_q_tot, axis=-1)
y = rewards + (1. - dones) * arglist.gamma * max_q_tot
# Predictions
action_one_hot = tf.one_hot(
tf.argmax(actions, axis=2, name='action_one_hot'),
no_actions)
q_values = model([state, adj_n])
q_tot = tf.reduce_sum(q_values * action_one_hot,
axis=-1,
name='q_acted')
pred = tf.reduce_sum(q_tot, axis=1)
if "huber" in arglist.loss_type:
loss = tf.reduce_sum(
u.huber_loss(pred, tf.stop_gradient(y)))
elif "mse" in arglist.loss_type:
loss = tf.losses.mean_squared_error(
pred, tf.stop_gradient(y))
else:
raise RuntimeError(
"Loss function should be either Huber or MSE. %s found!"
% arglist.loss_type)
gradients = tape.gradient(loss,
model.trainable_variables)
local_clipped = u.clip_by_local_norm(gradients, 0.1)
optimizer.apply_gradients(
zip(local_clipped, model.trainable_variables))
tf.saved_model.save(model, result_path)
# display training output
if train_step % arglist.save_rate == 0:
# eval_reward = get_eval_reward(env, model)
with open(res, "a+") as f:
mes_dict = {
"steps":
train_step,
"episodes":
len(episode_rewards),
"train_episode_reward":
np.round(np.mean(episode_rewards[-arglist.save_rate:]),
3),
# "eval_episode_reward": np.round(np.mean(eval_reward), 3),
"time":
round(time.time() - t_start, 3)
}
print(mes_dict)
for item in list(mes_dict.values()):
f.write("%s\t" % item)
f.write("\n")
f.close()
t_start = time.time()
# train target model
if arglist.soft_update:
weights = model.get_weights()
target_weights = model_t.get_weights()
for w in range(len(weights)):
target_weights[w] = arglist.tau * weights[w] + (
1 - arglist.tau) * target_weights[w]
model_t.set_weights(target_weights)
elif terminal and train_step % 200 == 0:
model_t.set_weights(model.get_weights())
def main(arglist):
global no_actions, no_features, no_agents
env = u.make_env(arglist.scenario, arglist.no_agents)
env.discrete_action_input = True
obs_shape_n = env.observation_space
no_agents = env.n
batch_size = arglist.batch_size
epsilon = arglist.epsilon
epsilon_decay = arglist.epsilon_decay
min_epsilon = arglist.min_epsilon
max_epsilon = arglist.max_epsilon
u.create_seed(arglist.seed)
# Velocity.x Velocity.y Pos.x Pos.y {Land.Pos.x Land.Pos.y}*10 {Ent.Pos.x Ent.Pos.y}*9
no_features = obs_shape_n[0].shape[0]
no_actions = env.action_space[0].n
model, model_t = __build_conf()
optimizer = tf.keras.optimizers.Adam(lr=arglist.lr)
# Results
episode_rewards = [0.0] # sum of rewards for all agents
result_path = os.path.join("results", arglist.exp_name)
res = os.path.join(result_path, "%s.csv" % arglist.exp_name)
if not os.path.exists(result_path):
os.makedirs(result_path)
replay_buffer = ReplayBuffer(arglist.max_buffer_size) # Init Buffer
episode_step = 0
train_step = 0
t_start = time.time()
obs_n = env.reset()
obs_n = u.reshape_state(obs_n, arglist.history_size)
print('Starting iterations...')
while True:
episode_step += 1
terminal = (episode_step >= arglist.max_episode_len)
predictions = get_predictions(u.to_tensor(np.array(obs_n)), model)
actions = get_actions(predictions, epsilon)
# Observe next state, reward and done value
try:
new_obs_n, rew_n, done_n, _ = env.step(actions)
except:
print(actions)
RuntimeError('Actions error!')
new_obs_n = u.refresh_history(np.copy(obs_n), new_obs_n)
done = all(done_n) or terminal
cooperative_reward = rew_n[0]
# Store the data in the replay memory
replay_buffer.add(obs_n, actions, cooperative_reward, new_obs_n, done)
obs_n = np.copy(new_obs_n)
episode_rewards[-1] += cooperative_reward
if done or terminal:
obs_n = env.reset()
obs_n = u.reshape_state(obs_n, arglist.history_size)
if arglist.decay_mode.lower() == "linear":
# straight line equation wrapper by max operation -> max(min_value,(-mx + b))
epsilon = np.amax(
(min_epsilon,
-((max_epsilon - min_epsilon) * train_step /
arglist.max_episode_len) / arglist.e_lin_decay + 1.0))
elif arglist.decay_mode.lower() == "exp":
# exponential's function Const(e^-t) wrapped by a min function
epsilon = np.amin(
(1, (min_epsilon + (max_epsilon - min_epsilon) *
np.exp(-(train_step / arglist.max_episode_len - 1) /
epsilon_decay))))
else:
epsilon = min_epsilon + (max_epsilon - min_epsilon) * np.exp(
-epsilon_decay * train_step / arglist.max_episode_len)
episode_step = 0
episode_rewards.append(0)
# increment global step counter
train_step += 1
# for displaying learned policies
if arglist.display:
time.sleep(0.1)
env.render()
continue
# Train the models
if replay_buffer.can_provide_sample(
batch_size, arglist.max_episode_len) and train_step % 100 == 0:
state, actions, rewards, new_state, dones = replay_buffer.sample(
batch_size)
# Calculate TD-target. The Model.predict() method returns numpy() array without taping the forward pass.
target_q_values = model_t(reformat_input(new_state))
# Apply max(Q) to obtain the TD-target
target_q_tot = tf.reduce_max(target_q_values, axis=-1)
# Apply VDN to reduce the agent-dimension
max_q_tot = tf.reduce_sum(target_q_tot, axis=-1)
y = rewards + (1. - dones) * arglist.gamma * max_q_tot
with tf.GradientTape() as tape:
# Predictions
action_one_hot = tf.one_hot(actions,
no_actions,
name='action_one_hot')
q_values = model(reformat_input(state))
q_tot = tf.reduce_sum(q_values * action_one_hot,
axis=-1,
name='q_acted')
pred = tf.reduce_sum(q_tot, axis=1)
if "huber" in arglist.loss_type:
# Computing the Huber Loss
loss = tf.reduce_sum(
u.huber_loss(pred, tf.stop_gradient(y)))
elif "mse" in arglist.loss_type:
# Computing the MSE loss
loss = tf.losses.mean_squared_error(
pred, tf.stop_gradient(y))
gradients = tape.gradient(loss, model.trainable_variables)
local_clipped = u.clip_by_local_norm(gradients, 0.1)
optimizer.apply_gradients(
zip(local_clipped, model.trainable_variables))
tf.saved_model.save(model, result_path)
# display training output
if train_step % arglist.save_rate == 0:
eval_reward = get_eval_reward(env, model)
with open(res, "a+") as f:
mes_dict = {
"steps":
train_step,
"episodes":
len(episode_rewards),
"train_episode_reward":
np.round(np.mean(episode_rewards[-arglist.save_rate:]),
3),
"eval_episode_reward":
np.round(np.mean(eval_reward), 3),
"loss":
round(loss.numpy(), 3),
"time":
round(time.time() - t_start, 3)
}
print(mes_dict)
for item in list(mes_dict.values()):
f.write("%s\t" % item)
f.write("\n")
f.close()
t_start = time.time()
# train target model
update_target_networks(model, model_t)
