def __init__(self,
name,
before_com_model,
channel,
after_com_model,
critic_mlp_model,
obs_shape_n,
act_space_n,
args,
local_q_func=False):
self.name = name
self.n = len(obs_shape_n)
self.args = args
self.alpha = 0.5
obs_ph_n = []
for i in range(self.n):
obs_ph_n.append(
U.BatchInput(obs_shape_n[i],
name="observation_" + str(i)).get())
# Create all the functions necessary to train the model
self.q_train, self.q_update, self.q_debug = q_train(
scope=self.name,
make_obs_ph_n=obs_ph_n,
act_space_n=act_space_n,
q_func=critic_mlp_model,
optimizer=tf.train.AdamOptimizer(learning_rate=args.lr),
grad_norm_clipping=0.5,
local_q_func=local_q_func,
num_units=args.num_units,
)
self.act, self.p_train, self.p_update, self.p_debug = self.p_train_function(
scope=self.name,
make_obs_ph_n=obs_ph_n,
act_space_n=act_space_n,
before_com_func=before_com_model,
channel=channel,
after_com_func=after_com_model,
q_func=critic_mlp_model,
optimizer=tf.train.AdamOptimizer(learning_rate=args.lr),
grad_norm_clipping=0.5,
local_q_func=local_q_func,
num_units=args.num_units,
beta=args.beta,
ibmac_com=args.ibmac_com,
)
# Create experience buffer
self.replay_buffer = ReplayBuffer(1e6)
# self.max_replay_buffer_len = 50 * args.max_episode_len
self.max_replay_buffer_len = args.batch_size * args.max_episode_len
self.replay_sample_index = None
self.message_1_for_record = []
def __init__(self,
name,
model,
obs_shape_n,
act_space_n,
agent_index,
args,
local_q_func=False):
self.name = name
self.n = len(obs_shape_n)
self.agent_index = agent_index
self.args = args
obs_ph_n = []
for i in range(self.n):
obs_ph_n.append(
U.BatchInput(obs_shape_n[i],
name="observation" + str(i)).get())
# Create all the functions necessary to train the model
self.q_train, self.q_update, self.q_debug = q_train(
scope=self.name,
make_obs_ph_n=obs_ph_n,
act_space_n=act_space_n,
q_index=agent_index,
q_func=model,
optimizer=tf.train.AdamOptimizer(learning_rate=args.lr),
grad_norm_clipping=0.5,
local_q_func=local_q_func,
num_units=args.num_units)
self.act, self.p_train, self.p_update, self.p_debug = p_train(
scope=self.name,
make_obs_ph_n=obs_ph_n,
act_space_n=act_space_n,
p_index=agent_index,
p_func=model,
q_func=model,
optimizer=tf.train.AdamOptimizer(learning_rate=args.lr),
grad_norm_clipping=0.5,
local_q_func=local_q_func,
num_units=args.num_units)
# Create experience buffer
self.replay_buffer = ReplayBuffer(1e6)
self.max_replay_buffer_len = args.batch_size * args.max_episode_len
self.replay_sample_index = None
class MADDPGAgentTrainer(AgentTrainer):
def __init__(self,
name,
model,
obs_shape_n,
act_space_n,
agent_index,
args,
local_q_func=False):
self.name = name
self.n = len(obs_shape_n)
self.agent_index = agent_index
self.args = args
obs_ph_n = []
for i in range(self.n):
obs_ph_n.append(
U.BatchInput(obs_shape_n[i],
name="observation" + str(i)).get())
# Create all the functions necessary to train the model
self.q_train, self.q_update, self.q_debug = q_train(
scope=self.name,
make_obs_ph_n=obs_ph_n,
act_space_n=act_space_n,
q_index=agent_index,
q_func=model,
optimizer=tf.train.AdamOptimizer(learning_rate=args.lr),
grad_norm_clipping=0.5,
local_q_func=local_q_func,
num_units=args.num_units)
self.act, self.p_train, self.p_update, self.p_debug = p_train(
scope=self.name,
make_obs_ph_n=obs_ph_n,
act_space_n=act_space_n,
p_index=agent_index,
p_func=model,
q_func=model,
optimizer=tf.train.AdamOptimizer(learning_rate=args.lr),
grad_norm_clipping=0.5,
local_q_func=local_q_func,
num_units=args.num_units)
# Create experience buffer
self.replay_buffer = ReplayBuffer(1e6)
self.max_replay_buffer_len = args.batch_size * args.max_episode_len
self.replay_sample_index = None
def action(self, obs):
return self.act(obs[None])[0]
def experience(self, obs, act, rew, new_obs):
# Store transition in the replay buffer.
self.replay_buffer.add(obs, act, rew, new_obs)
def preupdate(self):
self.replay_sample_index = None
def update(self, agents, t):
if len(
self.replay_buffer
) < self.max_replay_buffer_len: # replay buffer is not large enough
return
if not t % 100 == 0: # only update every 100 steps
return
self.replay_sample_index = self.replay_buffer.make_index(
self.args.batch_size)
# collect replay sample from all agents
obs_n = []
obs_next_n = []
act_n = []
index = self.replay_sample_index
for i in range(self.n):
obs, act, rew, obs_next = agents[i].replay_buffer.sample_index(
index)
obs_n.append(obs)
obs_next_n.append(obs_next)
act_n.append(act)
obs, act, rew, obs_next = self.replay_buffer.sample_index(index)
# train q network
num_sample = 1
target_q = 0.0
for i in range(num_sample):
target_act_next_n = [
agents[i].p_debug['target_act'](obs_next_n[i])
for i in range(self.n)
]
target_q_next = self.q_debug['target_q_values'](
*(obs_next_n + target_act_next_n))
target_q += rew + self.args.gamma * 1.0 * target_q_next
target_q /= num_sample
# train q network
q_loss = self.q_train(*(obs_n + act_n + [target_q]))
self.q_update()
# train p network
p_loss = self.p_train(*(obs_n + act_n))
self.p_update()
return [
q_loss, p_loss,
np.mean(target_q),
np.mean(rew),
np.mean(target_q_next),
np.std(target_q)
]
class IBMACAgentTrainer(AgentTrainer):
def __init__(self,
name,
before_com_model,
channel,
after_com_model,
critic_mlp_model,
obs_shape_n,
act_space_n,
args,
local_q_func=False):
self.name = name
self.n = len(obs_shape_n)
self.args = args
self.alpha = 0.5
obs_ph_n = []
for i in range(self.n):
obs_ph_n.append(
U.BatchInput(obs_shape_n[i],
name="observation_" + str(i)).get())
# Create all the functions necessary to train the model
self.q_train, self.q_update, self.q_debug = q_train(
scope=self.name,
make_obs_ph_n=obs_ph_n,
act_space_n=act_space_n,
q_func=critic_mlp_model,
optimizer=tf.train.AdamOptimizer(learning_rate=args.lr),
grad_norm_clipping=0.5,
local_q_func=local_q_func,
num_units=args.num_units,
)
self.act, self.p_train, self.p_update, self.p_debug = self.p_train_function(
scope=self.name,
make_obs_ph_n=obs_ph_n,
act_space_n=act_space_n,
before_com_func=before_com_model,
channel=channel,
after_com_func=after_com_model,
q_func=critic_mlp_model,
optimizer=tf.train.AdamOptimizer(learning_rate=args.lr),
grad_norm_clipping=0.5,
local_q_func=local_q_func,
num_units=args.num_units,
beta=args.beta,
ibmac_com=args.ibmac_com,
)
# Create experience buffer
self.replay_buffer = ReplayBuffer(1e6)
# self.max_replay_buffer_len = 50 * args.max_episode_len
self.max_replay_buffer_len = args.batch_size * args.max_episode_len
self.replay_sample_index = None
self.message_1_for_record = []
def action(self, obs_n, alpha, is_norm_training=False, is_inference=False):
obs = [obs[None] for obs in obs_n]
message_n = self.p_debug['check_message_n'](
*(list(obs) + [is_norm_training, is_inference]))
self.message_1_for_record.append(message_n[0])
if len(self.message_1_for_record) % 2500 == 0:
# print(np.var(self.message_1_for_record, axis=0))
# print(0.5 * np.log(2 * np.pi * np.mean(np.var(self.message_1_for_record, axis=0))) + 0.5)
self.message_1_for_record = []
self.alpha = alpha
return self.act(*(list(obs) + [is_norm_training, is_inference]))
def experience(self, obs, act, rew, new_obs, done, terminal):
# Store transition in the replay buffer.
self.replay_buffer.add(obs, act, rew, new_obs,
[float(d) for d in done])
def preupdate(self):
self.replay_sample_index = None
def update(self, agents, t):
if len(
self.replay_buffer
) < self.max_replay_buffer_len: # replay buffer is not large enough
return
if not t % 100 == 0: # only update every 100 steps
return
is_norm_training = True
is_inference = False
self.replay_sample_index = self.replay_buffer.make_index(
self.args.batch_size)
# collect replay sample from all agents
obs_n = []
obs_next_n = []
act_n = []
index = self.replay_sample_index
samples = self.replay_buffer.sample_index(index)
obs_n, act_n, rew_n, obs_next_n, done_n = [
np.swapaxes(item, 0, 1) for item in samples
]
# for i in range(self.n):
# obs, act, rew, obs_next, done = agents[i].replay_buffer.sample_index(index)
# obs_n.append(obs)
# obs_next_n.append(obs_next)
# act_n.append(act)
# obs, act, rew, obs_next, done = self.replay_buffer.sample_index(index)
# train q network
num_sample = 1
target_q = 0.0
# print(len(obs_next_n))
for i in range(num_sample):
target_act_next_n = self.p_debug['target_act'](
*(list(obs_next_n) + [is_norm_training, is_inference]))
target_q_next_n = self.q_debug['target_q_values'](
*(list(obs_next_n) + list(target_act_next_n) +
[is_norm_training, is_inference]))
target_q_n = [
rew + self.args.gamma * (1.0 - done) * target_q_next for rew,
done, target_q_next in zip(rew_n, done_n, target_q_next_n)
]
target_q_n = [target_q / num_sample for target_q in target_q_n]
q_loss = self.q_train(*(list(obs_n) + list(act_n) + target_q_n +
[is_norm_training, is_inference]))
# train p network
p_loss = self.p_train(*(list(obs_n) + list(act_n) +
[is_norm_training, is_inference]))
self.p_update()
self.q_update()
# p_values = self.p_debug['p_values'](*(list(obs_n)))
kl_loss = self.p_debug['kl_loss'](*(list(obs_n) + list(act_n) +
[is_norm_training, is_inference]))
# print('kl_loss', self.p_debug['kl_loss'](*(list(obs_n) + list(act_n))))
# if t % 5000 == 0:
# print('p_values', p_values[0][0])
# print('check_value', self.p_debug['p_values'](*(list(obs_n)))[0][0])
# print('check_mu', self.p_debug['check_mu'](*(list(obs_n)))[0][0])
# print('check_log', self.p_debug['check_log'](*(list(obs_n)))[0][0])
# print('kl_loss', kl_loss)
# message_n = self.p_debug['check_message_n'](*(list(obs_n)+[is_norm_training, is_inference]))
# hiddens_n = self.p_debug['check_hiddens_n'](*list(obs_n))
# print("message_n", message_n[0][0])
# for message in message_n:
# print("mean, var", np.mean(message, axis=0), np.var(message,axis=0))
# print("hiddens_n", hiddens_n[0][0])
# entropy = self.p_debug['check_entropy'](*list(obs_n))
# print("entropy",np.mean(entropy, (1,2)))
return [
q_loss, p_loss,
np.mean(target_q),
np.mean(rew_n),
np.mean(target_q_next_n),
np.std(target_q), kl_loss
]
def p_train_function(self,
make_obs_ph_n,
act_space_n,
before_com_func,
channel,
after_com_func,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
num_units=64,
scope="trainer",
reuse=None,
beta=0.05,
ibmac_com=True):
with tf.variable_scope(scope, reuse=reuse):
clip_threshold = 1 # 1, 5, 10
is_norm_training = tf.placeholder(tf.bool)
is_inference = tf.placeholder(tf.bool)
ibmac_nocom = not ibmac_com
num_agents = len(make_obs_ph_n)
# create distribtuions
act_pdtype_n = [
make_pdtype(act_space) for act_space in act_space_n
]
# set up placeholders
obs_ph_n = make_obs_ph_n
act_ph_n = [
act_pdtype_n[i].sample_placeholder([None],
name="action" + str(i))
for i in range(num_agents)
]
hiddens_n = [
before_com_func(obs_ph_n[i],
num_units,
scope="before_com_{}".format(i),
num_units=num_units) for i in range(num_agents)
]
before_com_vars_n = [
U.scope_vars(U.absolute_scope_name("before_com_{}".format(i)))
for i in range(num_agents)
]
hiddens_n_for_message = tf.concat([
before_com_func(obs_ph_n[i],
num_units,
scope="before_com_{}".format(i),
reuse=True,
num_units=num_units) for i in range(num_agents)
],
axis=1)
hiddens_n_for_message = tf.stop_gradient(hiddens_n_for_message)
channel_output = channel(hiddens_n_for_message,
num_units * num_agents,
scope="channel",
num_units=num_units * num_agents)
message_n, mu_message_n, logvar_message_n = [
tf.split(item, num_or_size_splits=num_agents, axis=1)
for item in channel_output
]
logvar_message_n = [
tf.clip_by_value(log, -10, 10) for log in logvar_message_n
] # constrain kl_loss not to be too large
message_n = [
clip_message(message, clip_threshold, is_norm_training,
is_inference) for message in message_n
]
channel_vars_n = [U.scope_vars(U.absolute_scope_name("channel"))]
if ibmac_nocom:
print('no_com')
p_n = [
after_com_func(hiddens_n[i],
int(act_pdtype_n[i].param_shape()[0]),
scope="p_func_{}".format(i),
num_units=num_units)
for i in range(num_agents)
]
else:
check_n = [
hiddens_n[i] + message_n[i] for i in range(num_agents)
]
p_n = [
after_com_func(hiddens_n[i] + message_n[i],
int(act_pdtype_n[i].param_shape()[0]),
scope="p_func_{}".format(i),
num_units=num_units)
for i in range(num_agents)
]
p_func_vars = [
U.scope_vars(U.absolute_scope_name("p_func_{}".format(i)))
for i in range(num_agents)
]
# wrap parameters in distribution
act_pd_n = [
act_pdtype_n[i].pdfromflat(p_n[i]) for i in range(num_agents)
]
act_sample_n = [act_pd.sample() for act_pd in act_pd_n]
p_reg_n = [
tf.reduce_mean(tf.square(act_pd.flatparam()))
for act_pd in act_pd_n
]
act_input_n_n = [act_ph_n + [] for _ in range(num_agents)]
for i in range(num_agents):
act_input_n_n[i][i] = act_pd_n[i].sample()
q_input_n = [
tf.concat(obs_ph_n + act_input_n, 1)
for act_input_n in act_input_n_n
]
q_n = [
q_func(q_input_n[i],
1,
scope="q_func_{}".format(i),
reuse=True,
num_units=num_units)[:, 0] for i in range(num_agents)
]
pg_loss_n = [-tf.reduce_mean(q) for q in q_n]
# # 0.25 =bandwidth
# kl_loss_message_n = [2 * (tf.pow(mu, 2) + tf.pow(tf.exp(log), 2)) - log + np.log(0.5) - 0.5 for mu, log in
# zip(mu_message_n, logvar_message_n)]
# #1
# kl_loss_message_n = [0.5 * (tf.pow(mu, 2) + tf.pow(tf.exp(log), 2)) - log - 0.5 for mu, log in
# zip(mu_message_n, logvar_message_n)]
# #5
# kl_loss_message_n = [1.0/50 * (tf.pow(mu, 2) + tf.pow(tf.exp(log), 2)) - log + np.log(5) - 0.5 for mu, log in
# zip(mu_message_n, logvar_message_n)]
# 10
# kl_loss_message_n = [1.0/200 * (tf.pow(mu, 2) + tf.pow(tf.exp(log), 2)) - log + np.log(10) - 0.5 for mu, log in
# zip(mu_message_n, logvar_message_n)]
##bw=1 b1+b2 = 1, alpha = bw
kl_loss_message_n = [
1 / 2 * 1 / (tf.pow(self.alpha, 2)) *
(tf.pow(mu, 2) + tf.pow(tf.exp(log), 2)) - log -
np.log(self.alpha) - 0.5
for mu, log in zip(mu_message_n, logvar_message_n)
]
entropy = [tf.exp(log) + 1.4189 for log in logvar_message_n]
pg_loss = tf.reduce_sum(pg_loss_n)
p_reg = tf.reduce_sum(p_reg_n)
kl_loss_message = tf.reduce_mean(kl_loss_message_n)
if ibmac_nocom:
loss = pg_loss + p_reg * 1e-3
else:
loss = pg_loss + p_reg * 1e-3 + beta * kl_loss_message
kl_loss = U.function(inputs=obs_ph_n + act_ph_n +
[is_norm_training, is_inference],
outputs=kl_loss_message)
var_list = []
var_list.extend(before_com_vars_n)
if not ibmac_nocom:
var_list.extend(channel_vars_n)
var_list.extend(p_func_vars)
var_list = list(itertools.chain(*var_list))
optimize_expr = U.minimize_and_clip(optimizer, loss, var_list,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n +
[is_norm_training, is_inference],
outputs=loss,
updates=[optimize_expr])
act = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=act_sample_n)
p_values = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=p_n)
if not ibmac_nocom:
check_values = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=check_n)
channel_com = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=channel_output)
check_mu = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=mu_message_n)
check_log = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=logvar_message_n)
else:
check_values = lambda x: 0
channel_com = lambda x: 0
check_mu = lambda x: 0
check_log = lambda x: 0
# target network
target_hiddens_n = [
before_com_func(obs_ph_n[i],
num_units,
scope="target_before_com_{}".format(i),
num_units=num_units) for i in range(num_agents)
]
target_before_com_vars = [
U.scope_vars(
U.absolute_scope_name("target_before_com_{}".format(i)))
for i in range(num_agents)
]
target_hiddens_n_for_message = tf.concat([
before_com_func(obs_ph_n[i],
num_units,
scope="target_before_com_{}".format(i),
reuse=True,
num_units=num_units) for i in range(num_agents)
],
axis=1)
target_hiddens_n_for_message = tf.stop_gradient(
target_hiddens_n_for_message)
target_channel_output = channel(target_hiddens_n_for_message,
num_units * num_agents,
scope="target_channel",
num_units=num_units * num_agents)
target_message_n, target_mu_message_n, target_logvar_message_n = [
tf.split(item, num_or_size_splits=num_agents, axis=1)
for item in target_channel_output
]
target_channel_vars = [
U.scope_vars(U.absolute_scope_name("target_channel"))
]
if ibmac_nocom:
target_p_n = [
after_com_func(target_hiddens_n[i],
int(act_pdtype_n[i].param_shape()[0]),
scope="target_p_func_{}".format(i),
num_units=num_units)
for i in range(num_agents)
]
else:
target_p_n = [
after_com_func(target_hiddens_n[i] + target_message_n[i],
int(act_pdtype_n[i].param_shape()[0]),
scope="target_p_func_{}".format(i),
num_units=num_units)
for i in range(num_agents)
]
# target_p_n = [after_com_func(tf.concat([target_hiddens_n[i],target_message_n[i]], axis=1), int(act_pdtype_n[i].param_shape()[0]), scope="target_p_func_{}".format(i), num_units=num_units) for i in range(num_agents)]
target_p_func_vars = [
U.scope_vars(
U.absolute_scope_name("target_p_func_{}".format(i)))
for i in range(num_agents)
]
target_var_list = []
target_var_list.extend(target_before_com_vars)
if not ibmac_nocom:
target_var_list.extend(target_channel_vars)
target_var_list.extend(target_p_func_vars)
target_var_list = list(itertools.chain(*target_var_list))
update_target_p = make_update_exp(var_list, target_var_list)
target_act_sample_n = [
act_pdtype_n[i].pdfromflat(target_p_n[i]).sample()
for i in range(num_agents)
]
target_act = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=target_act_sample_n)
check_message_n = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=message_n)
check_hiddens_n = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=hiddens_n)
check_entropy = U.function(inputs=obs_ph_n +
[is_norm_training, is_inference],
outputs=entropy)
return act, train, update_target_p, {
'p_values': p_values,
'target_act': target_act,
'kl_loss': kl_loss,
'check_values': check_values,
'channel_com': channel_com,
'check_mu': check_mu,
'check_log': check_log,
'check_message_n': check_message_n,
'check_hiddens_n': check_hiddens_n,
'check_entropy': check_entropy
}