def __init__(self, act_space, mem_units=64, name=""):
super().__init__(name="MDActorI" + name + "I")
self.act_space = act_space
self.act_pd = make_pdtype(act_space)
self.pd = None
self.output_layer = tf.keras.layers.Dense(act_space.n)
self.optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)
self.encoder_units = 64
self.read_units = 20
# MUST BE EQUAL
self.memory_units = mem_units
self.write_units = self.memory_units
self.action_units = 32
self.attention_units = 16
self.critic_units = 64
self.action_number = act_space.n
self.encode_layer = tf.keras.layers.Dense(self.encoder_units, tf.nn.relu)
self.read_projection = tf.keras.layers.Dense(self.read_units, tf.nn.relu)
self.read_layer = tf.keras.layers.Dense(self.read_units)
self.write_projection = tf.keras.layers.Dense(self.write_units, tf.nn.sigmoid)
self.write_layer = tf.keras.layers.Dense(self.write_units, tf.nn.tanh)
self.remember_layer = tf.keras.layers.Dense(self.memory_units, tf.nn.sigmoid)
self.forget_layer = tf.keras.layers.Dense(self.memory_units, tf.nn.sigmoid)
self.action_layer = tf.keras.layers.Dense(self.action_units, tf.nn.relu)
def q_train(make_obs_ph_n, act_space_n, q_index, q_func, optimizer, grad_norm_clipping=None, local_q_func=False, scope="trainer", reuse=None, num_units=64):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))]
target_ph = tf.placeholder(tf.float32, [None], name="target")
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[q_index], act_ph_n[q_index]], 1)
q = q_func(q_input, 1, scope="q_func", num_units=num_units)[:,0]
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
q_loss = tf.reduce_mean(tf.square(q - target_ph))
# viscosity solution to Bellman differential equation in place of an initial condition
q_reg = tf.reduce_mean(tf.square(q))
loss = q_loss #+ 1e-3 * q_reg
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars, grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph], outputs=loss, updates=[optimize_expr])
q_values = U.function(obs_ph_n + act_ph_n, q)
# target network
target_q = q_func(q_input, 1, scope="target_q_func", num_units=num_units)[:,0]
target_q_func_vars = U.scope_vars(U.absolute_scope_name("target_q_func"))
update_target_q = make_update_exp(q_func_vars, target_q_func_vars)
target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
return train, update_target_q, {'q_values': q_values, 'target_q_values': target_q_values}
def i_train(make_obs_ph_n, intent_ph_n, act_space_n, make_intent_ph_n, make_act_traj_ph_n, i_func, i_index,output_size , optimizer, scope, reuse, grad_norm_clipping=None, num_units=64):
with tf.variable_scope(scope, reuse=reuse):
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
obs_ph_n = make_obs_ph_n
#here the intent_ph can be used as the true actions, they are in the same shape
intent_ph_n = make_intent_ph_n
flat_act_traj_ph_n =[tf.reshape(a, (-1, a.shape[1] * a.shape[2] *a.shape[3])) for a in make_act_traj_ph_n]
act_traj_ph_n = make_act_traj_ph_n
i_input = [tf.concat([obs, act_traj], axis = 1) for obs, act_traj in zip(obs_ph_n, flat_act_traj_ph_n)]
i = i_func(i_input[i_index], output_size, scope = "i_func", num_units = 64 )
i_func_vars = U.scope_vars(U.absolute_scope_name("i_func"))
#define loss
loss = tf.reduce_mean(tf.square(i - intent_ph_n[i_index]))
optimize_expr = U.minimize_and_clip(optimizer, loss, i_func_vars, grad_norm_clipping)
train = U.function(inputs= obs_ph_n + act_traj_ph_n + intent_ph_n, outputs=loss, updates=[optimize_expr])
i_values = U.function(inputs =[obs_ph_n[i_index]] + [act_traj_ph_n[i_index]], outputs = i)
target_i = i_func(i_input, output_size, scope = "target_i_func", num_units = 64 )
target_i_func_vars = U.scope_vars(U.absolute_scope_name("target_i_func"))
update_target_i = make_update_exp(i_func_vars, target_i_func_vars)
target_i_values = U.function(inputs = [obs_ph_n[i_index]] +[act_traj_ph_n[i_index]], outputs = target_i)
return i_values, train, update_target_i,{'i_values': i_values, 'target_i_values': target_i_values}
def p_train(env, make_obs_ph_n, act_space_n, p_index, vf_func, shana, q_func, optimizer, grad_norm_clipping=None, local_q_func=False, num_units=64, scope="trainer", reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))]
policy = shana(
env_spec=env,
af = 15,
of = 22,
K=2,
hidden_layer_sizes=(128, 128),
qf=q_func,
reg=0.001
)
act, log_pi = policy.actions_for(observations=make_obs_ph_n[p_index],
with_log_pis=True)
act_input_n = act_ph_n + []
p_func_vars = policy.get_params_internal()
q_input = tf.concat(obs_ph_n + act_input_n, 1)
vf_input = tf.concat(obs_ph_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="q_func", reuse=True, num_units=num_units)[:,0]
vf = q_func(vf_input, 1, scope="vf_func",reuse=True, num_units=num_units)[:,0]
vf_func_vars = U.scope_vars(U.absolute_scope_name("vf_func"))
pg_loss = tf.reduce_mean(log_pi * tf.stop_gradient(log_pi - q + vf))
p_reg = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES,
scope=policy.name)
loss = pg_loss + p_reg
vf_loss = 0.5 * tf.reduce_mean((vf - tf.stop_gradient(q - log_pi))**2)
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars, grad_norm_clipping)
mikoto = U.minimize_and_clip(optimizer, vf_loss, vf_func_vars, grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n, outputs=loss, updates=[optimize_expr])
misaka = U.function(inputs=obs_ph_n + act_ph_n, outputs=loss, updates=[mikoto])
# target network
target_p = shana(
env_spec=env,
af = 15,
of = 22,
K=2,
hidden_layer_sizes=(128, 128),
qf=q_func,
reg=0.001,
name = 'target_policy'
)
target_p_func_vars = target_p.get_params_internal()
target_vf = q_func(vf_input, 1, scope="target_vf_func", num_units=num_units)[:,0]
target_vf_func_vars = U.scope_vars(U.absolute_scope_name("target_vf_func"))
target_act_r, tar_log = target_p.actions_for(observations=obs_ph_n[p_index],with_log_pis=True)
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
upvf = make_update_exp(vf_func_vars, target_vf_func_vars)
target_act = U.function(inputs=[obs_ph_n[p_index]], outputs=target_act_r)
return policy.get_actions, train, misaka, update_target_p, upvf, {'target_act': target_act}
def pMA_train(make_obs_ph_n, make_memory_ph_n, act_space_n, p_index, p_func, q_func, optimizer, grad_norm_clipping=None, local_q_func=False, critic_units=64, scope="trainer", reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))]
# p_input = obs_ph_n[p_index]
memory_input = make_memory_ph_n[p_index]
num_agents = len(obs_ph_n)
p_input = [None] * (num_agents + 1)
for i in range(num_agents):
p_input[i] = obs_ph_n[i]
p_input[num_agents] = memory_input
p, memory_state = p_func(obs_ph_n, memory_input, int(act_pdtype_n[p_index].param_shape()[0]),
scope="pMA_func", reuse=reuse)
p_func_vars = U.scope_vars(U.absolute_scope_name("pMA_func"))
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
act_sample = act_pd.sample()
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
act_input_n = act_ph_n + []
act_input_n[p_index] = act_pd.sample()
q_input = tf.concat(obs_ph_n + act_input_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="qMA_func", reuse=True, num_units=critic_units)[:,0]
pg_loss = -tf.reduce_mean(q)
loss = pg_loss + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars, grad_norm_clipping)
# Create callable functions
train = U.function(inputs=p_input + act_ph_n, outputs=loss, updates=[optimize_expr])
act = U.function(inputs=p_input, outputs=act_sample)
memory_out = U.function(inputs=p_input, outputs=memory_state)
p_values = U.function(p_input, p)
# target network
target_p, target_memory = p_func(obs_ph_n, memory_input, int(act_pdtype_n[p_index].param_shape()[0]), scope="target_p_func", reuse=reuse)
target_p_func_vars = U.scope_vars(U.absolute_scope_name("target_pMA_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_act = U.function(inputs=p_input, outputs=target_act_sample)
return act, memory_out, train, update_target_p, {'p_values': p_values, 'target_act': target_act}
def m_train(act_space_n,
m_index,
m_func,
optimizer,
mut_inf_coef=0,
grad_norm_clipping=None,
scope="trainer",
reuse=None,
num_units=64):
return
with tf.variable_scope(scope, reuse=reuse):
# create distribtuions
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
# set up placeholders
obs_ph_n = 1
act_ph_n = [
act_pdtype_n[i].sample_placeholder([None], name="action" + str(i))
for i in range(len(act_space_n))
]
target_ph = tf.placeholder(tf.float32, [None], name="target")
m_input = [1]
m = m_func(m_input, 1, scope="m_func", num_units=num_units)[:, 0]
m_func_vars = U.scope_vars(U.absolute_scope_name("m_func"))
m_loss = tf.reduce_mean(tf.square(m - target_ph))
# viscosity solution to Bellman differential equation in place of an initial condition
m_reg = tf.reduce_mean(tf.square(m))
loss = m_loss #+ 1e-3 * q_reg
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph],
outputs=loss,
updates=[optimize_expr])
m_values = U.function(obs_ph_n + act_ph_n, m)
# target network
target_m = m_func(m_input,
1,
scope="target_m_func",
num_units=num_units)[:, 0]
target_m_func_vars = U.scope_vars(
U.absolute_scope_name("target_m_func"))
update_target_m = make_update_exp(m_func_vars, target_m_func_vars)
target_m_values = U.function(obs_ph_n + act_ph_n, target_m)
return train, update_target_m, {
'm_values': m_values,
'target_m_values': target_m_values
}
def __init__(self, act_space, n_units=64, name=""):
super().__init__(name="ActorI" + name + "I")
self.base_model = MLP2_Model(n_units, tf.nn.relu, name)
self.act_space = act_space
self.act_pd = make_pdtype(act_space)
self.pd = None
self.output_layer = tf.keras.layers.Dense(act_space.n)
self.optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)
def p_train(make_obs_ph_n, act_space_n, p_index, p_func, q_func, optimizer, grad_norm_clipping=None, local_q_func=False, num_units=64, scope="trainer", reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# create distribtuions
# Discrete type for spread
# SoftCategoricalPdType
# SoftCategoricalPd
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
#print('act_pdtype_n:\n',act_pdtype_n)
# set up placeholders
obs_ph_n = make_obs_ph_n
#print('obs_ph_n:\n', obs_ph_n)
act_ph_n = [act_pdtype_n[i].sample_placeholder([None], name="action"+str(i)) for i in range(len(act_space_n))]
#print('act_ph_n:\n', act_ph_n)
p_input = obs_ph_n[p_index]
p = p_func(inputs=p_input, num_outputs=int(act_pdtype_n[p_index].param_shape()[0]), scope="p_func", num_units=num_units, num_layers=3)
#print('p',p) #shape=(?, 2)
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
#print('act_pd:\n', act_pd)
act_sample = act_pd.sample()
#print('act_sample 1111111:\n', act_sample) #shape=(?, 2)
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
act_input_n = act_ph_n + []
act_input_n[p_index] = act_pd.sample()
q_input = tf.concat(obs_ph_n + act_input_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(inputs=q_input, num_outputs=1, scope="q_func", reuse=True, num_units=num_units, num_layers=3)[:,0]
pg_loss = -tf.reduce_mean(q)
loss = pg_loss + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars, grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n, outputs=loss, updates=[optimize_expr])
act = U.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
p_values = U.function([obs_ph_n[p_index]], p)
# target network
target_p = p_func(inputs=p_input, num_outputs=int(act_pdtype_n[p_index].param_shape()[0]), scope="target_p_func", num_units=num_units, num_layers=3)
target_p_func_vars = U.scope_vars(U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_act = U.function(inputs=[obs_ph_n[p_index]], outputs=target_act_sample)
return act, train, update_target_p, {'p_values': p_values, 'target_act': target_act}
def p_train(make_obs_ph_n, act_space_n, p_index, p_func, q_func, optimizer, grad_norm_clipping=None, local_q_func=False,
num_units=64, scope="trainer", reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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 # [U.ensure_tf_input(make_obs_ph_n[i]("observation"+str(i))).get() for i in range(len(make_obs_ph_n))]
act_ph_n = [act_pdtype_n[i].sample_placeholder([None], name="action" + str(i)) for i in range(len(act_space_n))]
p_input = obs_ph_n[p_index]
p = p_func(p_input, int(act_pdtype_n[p_index].param_shape()[0]), scope="p_func", num_units=num_units)
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
act_sample = act_pd.sample()
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
act_input_n = act_ph_n + []
act_input_n[p_index] = act_pd.sample() # act_pd.mode() #
q_input = tf.concat(obs_ph_n + act_input_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="q_func", reuse=True, num_units=num_units)[:, 0]
pg_loss = -tf.reduce_mean(q)
loss = pg_loss + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars, grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n, outputs=loss, updates=[optimize_expr])
act = U.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
p_values = U.function([obs_ph_n[p_index]], p)
# target network
target_p = p_func(p_input, int(act_pdtype_n[p_index].param_shape()[0]), scope="target_p_func",
num_units=num_units)
target_p_func_vars = U.scope_vars(U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
sync_target_p = make_update_exp(p_func_vars, target_p_func_vars, rate=1.0)
target_act_pd = act_pdtype_n[p_index].pdfromflat(target_p)
target_act_sample = target_act_pd.sample()
target_act_mode = target_act_pd.mode()
target_act = U.function(inputs=[obs_ph_n[p_index]], outputs=target_act_sample)
target_mode = U.function(inputs=[obs_ph_n[p_index]], outputs=target_act_mode)
target_p_values = U.function([obs_ph_n[p_index]], target_p)
return act, train, update_target_p, sync_target_p, {'p_values': p_values, 'target_p_values': target_p_values,
'target_mode': target_mode, 'target_act': target_act}
def q_train(n_agents, make_state_ph_n, make_obs_ph_n, act_space_n, q_index, q_func, optimizer, grad_norm_clipping=None, local_q_func=False,
scope="trainer", reuse=None, num_units=64, discrete_action=False, target_update_tau=0.001, use_global_state=False,
share_weights=False):
with tf.variable_scope(scope, reuse=reuse):
# create distribtuions
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
# set up placeholders
if not use_global_state:
obs_ph_n = make_obs_ph_n
else:
obs_ph_n = make_state_ph_n
act_ph_n = [act_pdtype_n[i].sample_placeholder([None], name="action"+str(i)) for i in range(len(act_space_n))]
target_ph = tf.placeholder(tf.float32, [None], name="target")
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[q_index], act_ph_n[q_index]], 1)
if share_weights:
# add agent id to input as layers share weights
q_input = tf.concat([q_input,
tf.tile(tf.eye(n_agents)[q_index:q_index+1],
[tf.shape(q_input)[0], 1])], -1)
q = q_func(q_input, 1, scope="q_func", reuse=share_weights, num_units=num_units,
constrain_out=False, discrete_action=discrete_action)[:, 0] #share_weights)[:, 0]
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
q_loss = tf.reduce_mean(tf.square(q - target_ph))
# viscosity solution to Bellman differential equation in place of an initial condition
q_reg = tf.reduce_mean(tf.square(q))
loss = q_loss #+ 1e-3 * q_reg
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars, grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph], outputs=loss, updates=[optimize_expr])
q_values = U.function(obs_ph_n + act_ph_n, q)
# target network
target_q = q_func(q_input, 1, scope="target_q_func", reuse=share_weights, num_units=num_units,
constrain_out=False, discrete_action=discrete_action)[:, 0]
target_q_func_vars = U.scope_vars(U.absolute_scope_name("target_q_func"))
update_target_q = make_update_exp(q_func_vars, target_q_func_vars, target_update_tau)
target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
return train, update_target_q, {'q_values': q_values, 'target_q_values': target_q_values}
def get_trainers(env, num_adversaries, obs_shape_n, arglist):
from maddpg.common.distributions import make_pdtype
trainers = []
act_info_n = [make_pdtype(ac) for ac in env.action_space]
for i in range(env.n):
local_q_func = (arglist.adv_policy
== "ddpg") if i < num_adversaries else (
arglist.good_policy == "ddpg")
trainer = MADDPG(obs_shape_n,
act_info_n,
i,
arglist,
local_q_func=local_q_func)
trainers.append(trainer)
return trainers
def p_train(env, make_obs_ph_n, act_space_n, p_index, vf_func, shana, q_func, optimizer, grad_norm_clipping=None, local_q_func=False, num_units=64, scope="trainer", reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))]
policy = shana(
env_spec=env,
af = 15,
of = 22,
K=2,
hidden_layer_sizes=(100, 100),
qf=q_func,
reg=0.001
)
actions, log_pi = policy.actions_for(observations=make_obs_ph_n[p_index],
with_log_pis=True)
print(actions)
print(log_pi)
p_func_vars = policy.get_params_internal()
q_input = tf.concat(obs_ph_n + act_input_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="q_func", reuse=True, num_units=num_units)[:,0]
pg_loss = -tf.reduce_mean(q)
p_reg = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES,
scope=policy.name)
loss = pg_loss + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars, grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n, outputs=loss, updates=[optimize_expr])
act = U.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
p_values = U.function([obs_ph_n[p_index]], p)
# target network
target_p = p_func(p_input, int(act_pdtype_n[p_index].param_shape()[0]), scope="target_p_func", num_units=num_units)
target_p_func_vars = U.scope_vars(U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_act = U.function(inputs=[obs_ph_n[p_index]], outputs=target_act_sample)
return act, train, update_target_p, {'p_values': p_values, 'target_act': target_act}
def __init__(self,
name,
learning_rate,
obs_shape_n,
act_space_n,
agent_index,
args,
local_q_func=False):
self.name = name
self.learning_rate = learning_rate
self.n = len(obs_shape_n)
self.agent_index = agent_index
self.obs_size = obs_shape_n[agent_index]
self.joint_obs_size = np.sum(obs_shape_n)
self.act_size = act_space_n[agent_index].n
self.act_pdtype_n = [
make_pdtype(act_space) for act_space in act_space_n
]
self.joint_act_size = 0
for i_act in act_space_n:
self.joint_act_size += i_act.n
self.args = args
self.actor = Actor(self.obs_size, self.act_size)
self.actor_target = Actor(self.obs_size, self.act_size)
self.critic = self.build_critic()
self.critic_target = self.build_critic()
update_target(self.actor, self.actor_target, 0)
update_target(self.critic, self.critic_target, 0)
#self.actor, self.critic = self.build_model()
#self.actor_target, self.critic_target = self.build_model()
self.actor_optimizer = self.build_actor_optimizer()
# 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
gpu = -1
self.device = "/gpu:{}".format(gpu) if gpu >= 0 else "/cpu:0"
def p_train(make_obs_ph_n, act_space_n, p_index, p_func, num_units=64, scope="trainer", reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))]
p_input = obs_ph_n[p_index]
p = p_func(p_input, int(act_pdtype_n[p_index].param_shape()[0]), scope="p_func", num_units=num_units)
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
act_sample = act_pd.sample()
# Create callable functions
act = U.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
return act
def p_train(make_obs_ph_n,
act_space_n,
p_index,
p_func,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
num_units=64,
scope="trainer",
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))
]
p_input = obs_ph_n[p_index]
p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="p_func",
num_units=num_units)
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
act_sample = act_pd.sample()
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
act_input_n = act_ph_n + []
act_input_n[p_index] = act_pd.sample()
q_input = tf.concat(obs_ph_n + act_input_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="q_func", reuse=True,
num_units=num_units)[:, 0]
pg_loss = -tf.reduce_mean(q)
# Gradient computation mods
# ---------------------------------------------------------------------------------------------
obs_flat_shape = [len(obs_ph_n) * int(obs_ph_n[0].shape[-1])]
act_flat_shape = [len(act_space_n) * int(act_space_n[0].shape[-1])]
obs_flat_ph = tf.placeholder(tf.float32,
shape=[None] + obs_flat_shape,
name="obs_flat_input")
act_flat_ph = tf.placeholder(tf.float32,
shape=[None] + act_flat_shape,
name="act_flat_input")
q_vec_input = tf.concat([obs_flat_ph, act_flat_ph], axis=-1)
serial_q = q_func(q_vec_input,
1,
scope="q_func",
reuse=True,
num_units=num_units)[:, 0]
# calculate gradient of serial q value wrt actions
raw_grad = tf.gradients(serial_q, act_flat_ph)
grad_norm = tf.divide(raw_grad, tf.norm(raw_grad))
grad_norm_value = U.function([obs_flat_ph, act_flat_ph], grad_norm)
# ---------------------------------------------------------------------------------------------
loss = pg_loss + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n,
outputs=loss,
updates=[optimize_expr])
act = U.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
p_values = U.function([obs_ph_n[p_index]], p)
# target network
target_p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="target_p_func",
num_units=num_units)
target_p_func_vars = U.scope_vars(
U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_act = U.function(inputs=[obs_ph_n[p_index]],
outputs=target_act_sample)
return act, train, update_target_p, {
'p_values': p_values,
'target_act': target_act,
'grad_norm_value': grad_norm_value
}
def q_train(make_obs_ph_n,
act_space_n,
q_index,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
scope="trainer",
reuse=None,
num_units=64):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))
]
target_ph = tf.placeholder(tf.float32, [None], name="target")
# get flattened obs and act shape
act_shape = tf.shape(act_ph_n)
act_serial = tf.concat(act_ph_n, 1)
act_serial = tf.reshape(act_serial,
[act_shape[1], act_shape[0] * act_shape[-1]])
act_serial_values = U.function(act_ph_n, act_serial)
obs_shape = tf.shape(obs_ph_n)
obs_serial = tf.concat(obs_ph_n, 1)
obs_serial = tf.reshape(obs_serial,
[obs_shape[1], obs_shape[0] * obs_shape[-1]])
obs_serial_values = U.function(obs_ph_n, obs_serial)
obs_flat_shape = [len(obs_ph_n) * int(obs_ph_n[0].shape[-1])]
act_flat_shape = [len(act_space_n) * int(act_space_n[0].shape[-1])]
obs_flat_ph = tf.placeholder(tf.float32,
shape=[None] + obs_flat_shape,
name="obs_flat_input")
act_flat_ph = tf.placeholder(tf.float32,
shape=[None] + act_flat_shape,
name="act_flat_input")
target_input = tf.concat([obs_flat_ph, act_flat_ph], axis=-1)
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[q_index], act_ph_n[q_index]], 1)
q = q_func(q_input, 1, scope="q_func", num_units=num_units)[:, 0]
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
q_loss = tf.reduce_mean(tf.square(q - target_ph))
# viscosity solution to Bellman differential equation in place of an initial condition
q_reg = tf.reduce_mean(tf.square(q))
loss = q_loss #+ 1e-3 * q_reg
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph],
outputs=loss,
updates=[optimize_expr])
q_values = U.function(obs_ph_n + act_ph_n, q)
# target network
# target_orig_q = q_func(q_input, 1, scope="target_orig_q_func", num_units=num_units)[:,0]
target_q = q_func(target_input,
1,
scope="target_q_func",
num_units=num_units)[:, 0]
target_q_func_vars = U.scope_vars(
U.absolute_scope_name("target_q_func"))
update_target_q = make_update_exp(q_func_vars, target_q_func_vars)
# target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
target_q_values = U.function([obs_flat_ph, act_flat_ph], target_q)
# calculate gradient of target q value wrt actions
raw_grad = tf.gradients(target_q, act_flat_ph)
grad_norm = tf.divide(raw_grad, tf.norm(raw_grad))
grad_norm_value = U.function([obs_flat_ph, act_flat_ph], grad_norm)
return train, update_target_q, {
'q_values': q_values,
'target_q_values': target_q_values,
'act_serial_values': act_serial_values,
'obs_serial_values': obs_serial_values,
'grad_norm_value': grad_norm_value
}
def dqn_train(make_obs_ph_n,
act_space_n,
p_index,
p_func,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func="dqn",
num_units=64,
scope="trainer",
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))
]
p_input = obs_ph_n[p_index]
p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="p_func",
num_units=num_units)
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
act_sample = act_pd.sample()
target_ph = tf.placeholder(tf.float32, [None], name="target")
tf_p = tf.reduce_sum(p, reduction_indices=1)
loss = tf.reduce_mean(tf.square(tf_p - target_ph))
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph],
outputs=loss,
updates=[optimize_expr])
act = U.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
p_values = U.function([obs_ph_n[p_index]], p)
# target network
target_p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="target_p_func",
num_units=num_units)
target_p_func_vars = U.scope_vars(
U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_act = U.function(inputs=[obs_ph_n[p_index]],
outputs=target_act_sample)
return act, train, update_target_p, {
'p_values': p_values,
'target_act': target_act
}
def q_train(make_obs_ph_n,
act_space_n,
q_index,
q_func,
u_func,
optimizer,
optimizer_lamda,
exp_var_alpha=None,
cvar_alpha=None,
cvar_beta=None,
grad_norm_clipping=None,
local_q_func=False,
scope="trainer",
reuse=None,
num_units=64,
u_estimation=False,
constrained=True,
constraint_type=None,
agent_type=None):
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
if constrained:
lamda_constraint = tf.get_variable(
'lamda_constraint' + str(q_index), [1],
initializer=tf.constant_initializer(1.0),
dtype=tf.float32)
if constraint_type == "CVAR":
v_constraint = tf.get_variable(
'v_constraint' + str(q_index), [1],
initializer=tf.constant_initializer(1.0),
dtype=tf.float32)
# create distribtuions
act_pdtype_n = make_pdtype(act_space_n[q_index])
# set up placeholders
obs_ph_n = make_obs_ph_n
act_ph_n = [act_pdtype_n.sample_placeholder([None], name="action0")]
target_ph = tf.placeholder(tf.float32, [None], name="target")
if u_estimation:
target_ph_u = tf.placeholder(tf.float32, [None], name="target_u")
rew = tf.placeholder(tf.float32, [None], name="reward")
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[0], act_ph_n[0]], 1)
q = q_func(q_input, 1, scope="q_func", num_units=num_units)[:, 0]
if u_estimation:
u_input = tf.concat(obs_ph_n + act_ph_n, 1)
u = u_func(u_input, 1, scope="u_func", num_units=num_units)[:, 0]
u_loss = tf.reduce_mean(
tf.square(
tf.square(rew) + 2 * tf.multiply(rew, target_ph) +
target_ph_u - u))
var = u - tf.square(q)
else:
var = tf.square(rew + target_ph) - tf.square(q)
if constrained:
if constraint_type == "Exp_Var":
#print ('In constraint generation with lamda alpha')
constraint = lamda_constraint * (var - exp_var_alpha)
q_loss = tf.reduce_mean(
tf.square(q - (target_ph + rew - constraint)))
elif constraint_type == "CVAR":
cvar = v_constraint + (1.0 /
(1.0 - cvar_beta)) * tf.reduce_mean(
tf.nn.relu(q - v_constraint))
constraint = lamda_constraint * (cvar_alpha - cvar)
q_loss = tf.reduce_mean(
tf.square(q - (target_ph + rew - constraint)))
else:
q_loss = tf.reduce_mean(tf.square(q - (target_ph + rew)))
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
if u_estimation:
u_func_vars = U.scope_vars(U.absolute_scope_name("u_func"))
# viscosity solution to Bellman differential equation in place of an initial condition
q_reg = tf.reduce_mean(tf.square(q))
train3 = None
if u_estimation:
loss = q_loss + u_loss #+ 1e-3 * q_reg
optimize_expr = U.minimize_and_clip(optimizer, loss,
q_func_vars + u_func_vars,
grad_norm_clipping)
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[target_ph_u] + [rew],
outputs=[q_loss, u_loss],
updates=[optimize_expr])
var_fn = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[target_ph_u] + [rew],
outputs=var)
elif constraint_type == "CVAR":
loss = q_loss
optimize_expr = U.minimize_and_clip(optimizer, loss,
q_func_vars + [v_constraint],
grad_norm_clipping)
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[rew],
outputs=q_loss,
updates=[optimize_expr])
cvar_fn = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[rew],
outputs=cvar)
var_fn = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[rew],
outputs=var)
else:
#print ('in loss minimization over q_func_vars')
loss = q_loss
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars,
grad_norm_clipping)
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[rew],
outputs=q_loss,
updates=[optimize_expr])
var_fn = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[rew],
outputs=var)
if not constrained:
optimize_expr3 = U.minimize_and_clip(optimizer, loss,
[v_constraint],
grad_norm_clipping)
train3 = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[rew],
outputs=q_loss,
updates=[optimize_expr3])
#loss = loss + 1e-4*q_reg
# Create callable functions
q_values = U.function(obs_ph_n + act_ph_n, q)
# target network
target_q = q_func(q_input,
1,
scope="target_q_func",
num_units=num_units)[:, 0]
target_q_func_vars = U.scope_vars(
U.absolute_scope_name("target_q_func"))
update_target_q = make_update_exp(q_func_vars, target_q_func_vars)
target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
if u_estimation:
u_values = U.function(obs_ph_n + act_ph_n, u)
target_u = u_func(u_input,
1,
scope="target_u_func",
num_units=num_units)[:, 0]
target_u_func_vars = U.scope_vars(
U.absolute_scope_name("target_u_func"))
update_target_u = make_update_exp(u_func_vars, target_u_func_vars)
target_u_values = U.function(obs_ph_n + act_ph_n, target_u)
if constrained:
loss2 = -loss
#print ('in loss maximisation over lamda')
optimize_expr2 = U.minimize_and_clip(optimizer_lamda, loss2,
[lamda_constraint],
grad_norm_clipping)
if u_estimation:
train2 = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[target_ph_u] + [rew],
outputs=loss2,
updates=[optimize_expr2])
else:
train2 = U.function(inputs=obs_ph_n + act_ph_n + [target_ph] +
[rew],
outputs=loss2,
updates=[optimize_expr2])
if not u_estimation:
update_target_u = None
target_u_values = None
u_values = None
if not constrained:
train2 = None
lamda_constraint = None
if constraint_type != "CVAR":
cvar_fn = None
v_constraint = None
return train, train2, train3, update_target_q, update_target_u, {
'q_values': q_values,
'u_values': u_values,
'target_q_values': target_q_values,
'target_u_values': target_u_values,
'var': var_fn,
'cvar': cvar_fn,
'lamda_constraint': lamda_constraint,
'v_constraint': v_constraint,
'optimize_expr': optimize_expr
}
def p_train_adv(make_obs_ph_n,
act_space_n,
p_index,
p_func,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
num_units=64,
scope="trainer",
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))
]
p_input = obs_ph_n[p_index]
p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="p_func",
num_units=num_units)
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
act_sample = act_pd.sample()
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
act_input_n = act_ph_n + []
# changed
sample = act_pd.sample()
act_input_n[p_index] = sample
q_input = tf.concat(obs_ph_n + act_input_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="q_func", reuse=True,
num_units=num_units)[:, 0]
## Modifications here
## Create values vector: auto solve rows by 1 column
v = tf.tile([0.0],
[tf.shape(sample)[0]]) # variable for value function
for i in range(act_space_n[p_index].n):
# create row tensor with ith element as 1, actions are one-hot
a = np.zeros((1, act_space_n[p_index].n), dtype=np.float32)
a[0, i] = 1
a = tf.convert_to_tensor(a)
# tile this row tensor automatic number of times
a = tf.tile(a, [tf.shape(sample)[0], 1])
act_input = act_ph_n + []
act_input[p_index] = tf.convert_to_tensor(a)
q_input_tmp = tf.concat(obs_ph_n + act_input, 1)
if local_q_func:
q_input_tmp = tf.concat(
[obs_ph_n[p_index], act_input_n[p_index]], 1)
# add Q(a[i], s) * pi(a[i]) to value
p_i = act_pd.logits[:, i]
# tmp is q values for action i multiplied by probability of taking action i
tmp = tf.multiply(
q_func(q_input_tmp,
1,
scope="q_func",
reuse=True,
num_units=num_units)[:, 0], p_i)
v = tf.add(v, tmp)
a = tf.subtract(v, q)
# loss is equal to advantage
pg_loss = -tf.reduce_mean(a)
## Modifications end
loss = pg_loss + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n,
outputs=loss,
updates=[optimize_expr])
act = U.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
p_values = U.function([obs_ph_n[p_index]], p)
# target network
target_p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="target_p_func",
num_units=num_units)
target_p_func_vars = U.scope_vars(
U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_act = U.function(inputs=[obs_ph_n[p_index]],
outputs=target_act_sample)
return act, train, update_target_p, {
'p_values': p_values,
'target_act': target_act
}
def q_train(make_obs_ph_n,
act_space_n,
q_index,
q_func,
optimizer,
adversarial,
adv_eps,
adv_eps_s,
num_adversaries,
grad_norm_clipping=None,
local_q_func=False,
scope="trainer",
reuse=None,
num_units=64):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))
]
target_ph = tf.placeholder(tf.float32, [None], name="target")
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[q_index], act_ph_n[q_index]], 1)
q = q_func(q_input, 1, scope="q_func", num_units=num_units)[:, 0]
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
q_loss = tf.reduce_mean(tf.square(q - target_ph))
# viscosity solution to Bellman differential equation in place of an initial condition
q_reg = tf.reduce_mean(tf.square(q))
loss = q_loss #+ 1e-3 * q_reg
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph],
outputs=loss,
updates=[optimize_expr])
q_values = U.function(obs_ph_n + act_ph_n, q)
# target network
target_q = q_func(q_input,
1,
scope="target_q_func",
num_units=num_units)[:, 0]
if adversarial:
num_agents = len(act_ph_n)
if q_index < num_adversaries:
adv_rate = [
adv_eps_s * (i < num_adversaries) + adv_eps *
(i >= num_adversaries) for i in range(num_agents)
]
else:
adv_rate = [
adv_eps_s * (i >= num_adversaries) + adv_eps *
(i < num_adversaries) for i in range(num_agents)
]
print(" adv rate for q_index : ", q_index, adv_rate)
pg_loss = -tf.reduce_mean(target_q)
raw_perturb = tf.gradients(pg_loss, act_ph_n)
perturb = [
adv_eps * tf.stop_gradient(tf.nn.l2_normalize(elem, axis=1))
for elem in raw_perturb
]
new_act_n = [
perturb[i] + act_ph_n[i] if i != q_index else act_ph_n[i]
for i in range(len(act_ph_n))
]
adv_q_input = tf.concat(obs_ph_n + new_act_n, 1)
target_q = q_func(adv_q_input,
1,
scope='target_q_func',
reuse=True,
num_units=num_units)[:, 0]
target_q_func_vars = U.scope_vars(
U.absolute_scope_name("target_q_func"))
update_target_q = make_update_exp(q_func_vars, target_q_func_vars)
target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
return train, update_target_q, {
'q_values': q_values,
'target_q_values': target_q_values
}
def p_train(make_obs_ph_n,
act_space_n,
p_index,
p_func,
q_func,
optimizer,
adversarial,
adv_eps,
adv_eps_s,
num_adversaries,
grad_norm_clipping=None,
local_q_func=False,
num_units=64,
scope="trainer",
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))
]
p_input = obs_ph_n[p_index]
p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="p_func",
num_units=num_units)
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
act_sample = act_pd.sample()
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
act_input_n = act_ph_n + []
act_input_n[p_index] = act_pd.sample()
q_input = tf.concat(obs_ph_n + act_input_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="q_func", reuse=True,
num_units=num_units)[:, 0]
pg_loss = -tf.reduce_mean(q)
if adversarial:
num_agents = len(act_input_n)
if p_index < num_adversaries:
adv_rate = [
adv_eps_s * (i < num_adversaries) + adv_eps *
(i >= num_adversaries) for i in range(num_agents)
]
else:
adv_rate = [
adv_eps_s * (i >= num_adversaries) + adv_eps *
(i < num_adversaries) for i in range(num_agents)
]
print(" adv rate for p_index : ", p_index, adv_rate)
raw_perturb = tf.gradients(pg_loss, act_input_n)
perturb = [
tf.stop_gradient(tf.nn.l2_normalize(elem, axis=1))
for elem in raw_perturb
]
perturb = [perturb[i] * adv_rate[i] for i in range(num_agents)]
new_act_n = [
perturb[i] + act_input_n[i] if i != p_index else act_input_n[i]
for i in range(len(act_input_n))
]
adv_q_input = tf.concat(obs_ph_n + new_act_n, 1)
adv_q = q_func(adv_q_input,
1,
scope="q_func",
reuse=True,
num_units=num_units)[:, 0]
pg_loss = -tf.reduce_mean(q)
loss = pg_loss + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n,
outputs=loss,
updates=[optimize_expr])
act = U.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
p_values = U.function([obs_ph_n[p_index]], p)
# target network
target_p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="target_p_func",
num_units=num_units)
target_p_func_vars = U.scope_vars(
U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_act = U.function(inputs=[obs_ph_n[p_index]],
outputs=target_act_sample)
return act, train, update_target_p, {
'p_values': p_values,
'target_act': target_act
}
def p_train(make_obs_ph_n,
act_space_n,
p_index,
p_func,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
num_units=64,
scope="trainer",
reuse=None):
with tf.variable_scope(scope, reuse=reuse): # 重用变量
# create distribtuions初始动作概率分布列表
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n
] # 为所有agent的动作空间都创造一个动作概率分布类
# 类的集合
# set up placeholders
obs_ph_n = make_obs_ph_n # 所有的agent观察到的环境信息
act_ph_n = [
act_pdtype_n[i].sample_placeholder([None], name="action" + str(i))
for i in range(len(act_space_n))
]
# 返回用于存放每个agent的动作的占位符集合，用于填充所有agent选择的动作[none]代表可以填入无数组数据
p_input = obs_ph_n[p_index] # 仅观察到自身周围环境
p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="p_func",
num_units=num_units)
# 建立神经网络，输出单元数为动作个数...这代码写的太呆了 输出每一个动作的值
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# 获取该神经网络全部变量
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
act_sample = act_pd.sample() # 确定性动作叠加噪声进行探索，成为随机策略，得到一组act，作用未知
p_reg = tf.reduce_mean(tf.square(
act_pd.flatparam())) # flatparam是所有动作的actor网络输出值的集合
# 猜测引入p_reg是因为预测其agent动作的需要
act_input_n = act_ph_n + []
act_input_n[p_index] = act_pd.sample(
) # 仅替换自己的动作输入，自己的动作来自于自己的policy网络输出
# 所以通过这一步将两个网络连接，通过q网络优化自己的policy网络
q_input = tf.concat(obs_ph_n + act_input_n,
1) # q输入所有的环境观察值与所有的agents采取的动作
# q的输入
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="q_func", reuse=True,
num_units=num_units)[:, 0]
# 这里是用的q_func由于reuse所以使用已经创建好的变量，即自己的q网络而不是再创建一个
# q_train,p_train属于同一个scope！
# 策略优化目标
pg_loss = -tf.reduce_mean(q) # loss与p_reg均需要加-号进行优化
# 目标使q的均值最大，等于采样后的-reduce_mean最小
loss = pg_loss + p_reg * 1e-3 # 引入熵？
# 梯度下降优化器节点表达式
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars,
grad_norm_clipping)
# Create callable functions可调用函数，批量使用session训练
train = U.function(inputs=obs_ph_n + act_ph_n,
outputs=loss,
updates=[optimize_expr])
act = U.function(inputs=[obs_ph_n[p_index]],
outputs=act_sample) # 依据自身观察给出确定性动作
p_values = U.function([obs_ph_n[p_index]], p) # 输出的是动作值集合
# target network
target_p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="target_p_func",
num_units=num_units)
target_p_func_vars = U.scope_vars(
U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_act = U.function(inputs=[obs_ph_n[p_index]],
outputs=target_act_sample)
return act, train, update_target_p, {
'p_values': p_values,
'target_act': target_act
}
def q_train(name,
make_obs_ph_n,
adj_n,
act_space_n,
num_adversaries,
neighbor_n,
q_func,
agent_n,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
reuse=None,
scope="trainer",
num_units=64):
with tf.variable_scope(scope, reuse=reuse):
# create distribtuions
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
# number of agents in this species
agent_n_species = num_adversaries if name == "adversaries" else agent_n - num_adversaries
# 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(len(act_space_n))
]
target_ph = [
tf.placeholder(tf.float32, [None], name="target")
for _ in range(agent_n_species)
]
q = []
q_square = []
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
for a in range(agent_n_species):
temp = q_func(q_input,
1,
scope="q_func_%d" % a,
num_units=num_units)[:, 0]
q.append(temp)
# q1 = tf.stack([q[i] for i in range(agent_n_species)], axis=1)
# q_square = [tf.square(tf.reduce_mean(q[i] - target_ph[i], axis=1)) for i in range(agent_n_species)]
q_func_vars = [
U.scope_vars(U.absolute_scope_name("q_func_%d" % i))
for i in range(agent_n_species)
]
q_loss = [
tf.reduce_mean(tf.square(q[i] - target_ph[i]))
for i in range(agent_n_species)
]
# viscosity solution to Bellman differential equation in place of an initial condition
# q_reg = tf.reduce_mean(tf.square(q1))
loss = q_loss
# + 1e-3 * q_reg
optimize_expr = [
U.minimize_and_clip(optimizer, loss[i], q_func_vars[i],
grad_norm_clipping)
for i in range(agent_n_species)
]
# Create callable functions
train = [
U.function(inputs=obs_ph_n + act_ph_n + [target_ph[i]],
outputs=loss[i],
updates=[optimize_expr[i]])
for i in range(agent_n_species)
]
q_values = U.function(obs_ph_n + act_ph_n, q)
# target network
target_q = []
for a in range(agent_n_species):
temp = q_func(q_input,
1,
scope="target_q_func_%d" % a,
num_units=num_units)[:, 0]
target_q.append(temp)
target_q_func_vars = [
U.scope_vars(U.absolute_scope_name("target_q_func_%d" % i))
for i in range(agent_n_species)
]
update_target_q = make_update_exp(q_func_vars,
target_q_func_vars,
central=False)
target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
return train, update_target_q, q_values, target_q_values
def q_train(make_obs_ph_n,
act_space_n,
q_index,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
scope="trainer",
reuse=None,
num_units=64):
with tf.variable_scope(scope, reuse=reuse):
# create distribtuions
# make_ob_ph_n是输入的placeholder，与obs_n同shape
act_pdtype_n = [make_pdtype(act_space)
for act_space in act_space_n] # 获取概率类型，传入动作维度（5）
# act_space来自于env.act_space,由实验环境决定
# 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(len(act_space_n))
]
target_ph = tf.placeholder(tf.float32, [None],
name="target") # 一维输入占位符
# 以上为三个placeholder, [None]增加维度，不知道喂进去多少数据时使用, 即None是batchsize大小
q_input = tf.concat(obs_ph_n + act_ph_n,
1) # q函数输入网络为动作加上环境，在1维上，即q网络输入是所有agent观察和动作
if local_q_func: # 用ddpg时即只用自己的行为训练
q_input = tf.concat([obs_ph_n[q_index], act_ph_n[q_index]], 1)
q = q_func(q_input, 1, scope="q_func",
num_units=num_units)[:, 0] # 取所有行的第0个数据
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
# q网络变量集合
q_loss = tf.reduce_mean(
tf.square(q - target_ph)) # target_ph 会被什么占据呢？ 会被喂进去的td target占据
# q网络的损失函数，均方差，target_ph来自于target网络的预测
# viscosity solution to Bellman differential equation in place of an initial condition
q_reg = tf.reduce_mean(tf.square(q))
loss = q_loss # + 1e-3 * q_reg
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars,
grad_norm_clipping)
# 优化器表达式，以及是否梯度clip
# Create callable functions
# theano function
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph],
outputs=loss,
updates=[optimize_expr])
q_values = U.function(obs_ph_n + act_ph_n, q)
# target network
target_q = q_func(q_input,
1,
scope="target_q_func",
num_units=num_units)[:, 0]
target_q_func_vars = U.scope_vars(
U.absolute_scope_name("target_q_func"))
update_target_q = make_update_exp(q_func_vars, target_q_func_vars)
target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
# 以下返回值均为theano function可以直接填入传入placeholder的参数
return train, update_target_q, {
'q_values': q_values,
'target_q_values': target_q_values
}
def c_next(make_obs_ph,
act_space,
c_ph,
c_next_func,
num_constraints,
optimizer,
grad_norm_clipping,
num_units=64,
reuse=False,
scope="c_next"):
with tf.variable_scope(scope, reuse=reuse):
# set up placeholders
act_pdtype = make_pdtype(act_space[0])
obs_ph = make_obs_ph
act_ph = act_pdtype.sample_placeholder([None], name="action")
c_next_target_ph = []
for _ in range(num_constraints):
c_next_target_ph.append(
tf.placeholder(tf.float32, [None, 1], name="target" + str(_)))
c_next_input = tf.concat(obs_ph, 1)
gs_ = []
for _ in range(num_constraints):
gs_.append(
c_next_func(c_next_input,
int((act_pdtype.param_shape()[0]) / 2),
scope="c_next_func" + str(_),
num_units=num_units))
c_ = [] # to be testified
for _ in range(num_constraints):
temp = c_ph[_] + tf.multiply(gs_[_], act_ph)
c_.append(tf.reduce_sum(temp, -1))
c_next_vars = [
U.scope_vars(U.absolute_scope_name("c_next_func" + str(_)))
for _ in range(num_constraints)
]
diff = [(c_[_] - c_next_target_ph[_]) for _ in range(num_constraints)]
c_next_loss = [
tf.reduce_mean(tf.square(diff[_])) for _ in range(num_constraints)
]
optimize_expr = [
U.minimize_and_clip(optimizer, c_next_loss[_], c_next_vars[_],
grad_norm_clipping)
for _ in range(num_constraints)
]
# Create callable functions
train = [
U.function(inputs=[obs_ph] + [act_ph] + [c_ph[_]] +
[c_next_target_ph[_]],
outputs=c_next_loss[_],
updates=[optimize_expr[_]])
]
c_next_values = [
U.function([obs_ph] + [act_ph] + [c_ph[_]], c_[_])
for _ in range(num_constraints)
]
g_next_values = [
U.function([obs_ph], gs_[_]) for _ in range(num_constraints)
]
return train, c_next_values, g_next_values
def p_train(name,
make_obs_ph_n,
adj_n,
act_space_n,
neighbor_n,
p_index,
p_func,
q_func,
num_adversaries,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
num_units=128,
scope="trainer",
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))
]
agent_n = len(obs_ph_n)
vec_n = U.BatchInput([1, neighbor_n], name="vec").get()
p_input1 = obs_ph_n[
0:num_adversaries] if name == "adversaries" else obs_ph_n[
num_adversaries:agent_n]
p_input2 = adj_n[0:num_adversaries] if name == "adversaries" else adj_n[
num_adversaries:agent_n]
p_input3 = vec_n
# call for actor network
# act_space is not good!!!!!!!!!!
p = p_func(p_input1,
p_input2,
p_input3,
neighbor_n,
num_adversaries if name == "adversaries" else
(agent_n - num_adversaries),
5,
scope="p_func",
num_units=num_units)
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# wrap parameters in distribution
act_pd = []
act_sample = []
for i in range(0, num_adversaries) if name == "adversaries" else range(
num_adversaries, agent_n):
act_pd_temp = act_pdtype_n[i].pdfromflat(
p[i - (0 if name == "adversaries" else num_adversaries)])
act_pd.append(act_pd_temp)
act_sample.append(act_pd_temp.sample())
temp = []
for i in range(len(act_pd)):
temp.append(act_pd[i].flatparam())
# Is this regularization method correct?????????????????????????????/
p_reg = tf.reduce_mean(tf.square(temp))
act_input_n = act_ph_n + []
for i in range(0, num_adversaries) if name == "adversaries" else range(
num_adversaries, agent_n):
act_input_n[i] = act_sample[
i - (0 if name == "adversaries" else num_adversaries)]
q_input = tf.concat(obs_ph_n + act_input_n, 1)
q = []
q_reduce_mean = []
for a in range(0, num_adversaries) if name == "adversaries" else range(
num_adversaries, agent_n):
index = a if name == "adversaries" else a - num_adversaries
temp = q_func(q_input,
1,
scope="q_func_%d" % index,
reuse=True,
num_units=num_units)[:, 0]
q.append(temp)
q_reduce_mean += temp
pg_loss = -tf.reduce_mean(q)
loss = pg_loss + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n + adj_n + [vec_n],
outputs=loss,
updates=[optimize_expr])
act = U.function(inputs=p_input1 +
(adj_n[0:num_adversaries] if name == "adversaries"
else adj_n[num_adversaries:agent_n]) + [p_input3],
outputs=act_sample,
list_output=True)
p_values = U.function(
p_input1 + (adj_n[0:num_adversaries] if name == "adversaries" else
adj_n[num_adversaries:agent_n]) + [p_input3],
p,
list_output=True)
# target network
target_p = p_func(p_input1,
p_input2,
p_input3,
neighbor_n,
num_adversaries if name == "adversaries" else
(agent_n - num_adversaries),
5,
scope="target_p_func",
num_units=num_units)
target_p_func_vars = U.scope_vars(
U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars,
target_p_func_vars,
central=True)
target_act_sample = []
for i in range(0, num_adversaries) if name == "adversaries" else range(
num_adversaries, agent_n):
target_act_sample.append(act_pdtype_n[i].pdfromflat(target_p[i - (
0 if name == "adversaries" else num_adversaries)]).sample())
target_act = U.function(
inputs=p_input1 +
(adj_n[0:num_adversaries] if name == "adversaries" else
adj_n[num_adversaries:agent_n]) + [p_input3],
outputs=target_act_sample,
list_output=True)
return act, train, update_target_p, p_values, target_act
def q_train(make_obs_ph_n,
act_space_n,
make_obs_history_n,
make_act_history_n,
q_index,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
scope="trainer",
reuse=None,
num_units=64):
"""
Q-Learning
make_obs_ph_n (tf.placeholder): Placeholder for the observation space of all agents
act_space_n (list): A list of the action spaces for all agents
make_obs_history_n (tf.placeholder): Placeholder for the observation history of all agents
make_act_history_n (tf.placeholder): Placeholder for the action space history of all agents
q_index (int): Agent index number
q_func (function): MLP Neural Network model for the agent.
optimizer (function): Network Optimizer function
grad_norm_clipping (float): Value by which to clip the norm of the gradient
local_q_func (boolean): Flag for using local q function
num_units (int): The number outputs for the layers of the model
scope (str): The name of the scope
reuse (boolean): Flag specifying whether to reuse the scope
Returns:
train (function): Training function for Q network
update_target_q (function): Update function for updating Q network values
q_debug (dict): Contains 'q_values' and 'target_q_values' of the Q network
"""
with tf.variable_scope(scope, reuse=reuse):
# 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
obs_history_n = make_obs_history_n
act_history_n = make_act_history_n
act_ph_n = [
act_pdtype_n[i].sample_placeholder([None], name="action" + str(i))
for i in range(len(act_space_n))
]
target_ph = tf.placeholder(tf.float32, [None], name="target")
# obs_ph_n = [tf.concat(3*[x],1,name="observation{}".format(i)) for i,x in enumerate(obs_ph_n)]
# act_ph_n = [tf.concat(3*[x],1,name="action{}".format(i)) for i,x in enumerate(act_ph_n)]
# Original implementation
# q_input = tf.concat(obs_ph_n + act_ph_n, 1)
# Modified
# Current plus 2 previous time-steps
q_input = tf.concat(
obs_ph_n + obs_history_n + act_ph_n + act_history_n, 1)
if local_q_func:
# Only have observations about myself when 'ddpg'
# Importantly... self position is relative to prey
q_input = tf.concat([obs_ph_n[q_index], act_ph_n[q_index]], 1)
q = q_func(q_input, 1, scope="q_func", num_units=num_units)[:, 0]
q_func_vars = tf_util.scope_vars(tf_util.absolute_scope_name("q_func"))
# ************************************************************************************************
# ccm_input = data for ccm
# ccm_value = ccm_func(ccm_input)
# ************************************************************************************************
# q_loss = tf.reduce_mean(tf.square(q - target_ph)) - ccm_loss
q_loss = tf.reduce_mean(tf.square(q - target_ph))
# Viscosity solution to Bellman differential equation in place of an initial condition
q_reg = tf.reduce_mean(tf.square(q))
# loss = q_loss + 1e-3 * q_reg
loss = q_loss
optimize_expr = tf_util.minimize_and_clip(optimizer, loss, q_func_vars,
grad_norm_clipping)
# Create callable functions
train = tf_util.function(inputs=obs_ph_n + obs_history_n + act_ph_n +
act_history_n + [target_ph],
outputs=loss,
updates=[optimize_expr])
q_values = tf_util.function(
obs_ph_n + obs_history_n + act_ph_n + act_history_n, q)
# Target network
target_q = q_func(q_input,
1,
scope="target_q_func",
num_units=num_units)[:, 0]
target_q_func_vars = tf_util.scope_vars(
tf_util.absolute_scope_name("target_q_func"))
update_target_q = make_update_exp(q_func_vars, target_q_func_vars)
target_q_values = tf_util.function(
obs_ph_n + obs_history_n + act_ph_n + act_history_n, target_q)
return train, update_target_q, {
'q_values': q_values,
'target_q_values': target_q_values
}
def p_train_recurrent(make_obs_ph_n,
make_state_ph_n,
make_obs_next_n,
make_obs_pred_n,
act_space_n,
p_index,
p_policy,
p_predict,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
num_units=64,
scope="trainer",
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# create distribtuions
# set up placeholders
obs_ph_n = make_obs_ph_n # all obs, in shape Agent_num * batch_size * time_step * obs_shape
obs_next_n = make_obs_next_n
state_ph_n = make_state_ph_n
obs_pred_n = make_obs_pred_n
# used for action
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
act_ph_n = [
act_pdtype_n[i].sample_placeholder([None], name="action" + str(i))
for i in range(len(act_space_n))
]
# p_input is local obs of an agent
obs_input = obs_ph_n[p_index]
state_input = state_ph_n[p_index]
act_input = act_ph_n[p_index]
obs_next = obs_next_n[p_index]
obs_pred_input = obs_pred_n[p_index]
# get output and state
p, gru_out, state = p_policy(
obs_input,
state_input,
obs_pred_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="p_policy",
num_units=num_units)
act_pd = act_pdtype_n[p_index].pdfromflat(
p) # wrap parameters in distribution
act_sample = act_pd.sample() # sample an action
# predict the next obs
obs_pred = p_predict(act_input,
gru_out,
int(obs_input.shape[1]),
scope="p_predict",
num_units=num_units)
# variables for optimization
p_func_vars = U.scope_vars(
U.absolute_scope_name("p_policy")) + U.scope_vars(
U.absolute_scope_name("p_predict"))
pred_loss = tf.reduce_mean(tf.square(obs_next -
obs_pred)) # predict loss
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam())) # reg item
# use critic net to get the loss about policy
act_input_n = act_ph_n + []
act_input_n[p_index] = act_pd.sample(
) # only modify the action of this agent
q_input = tf.concat(
obs_ph_n + act_input_n,
1) # get the input for Q net (all obs + all action)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="q_func", reuse=True,
num_units=num_units)[:, 0] # get q values
pg_loss = -tf.reduce_mean(q) # calculate loss to maximize Q values
loss = pg_loss + p_reg * 1e-3 + pred_loss * 1e-3
optimize_expr = U.minimize_and_clip(
optimizer, loss, p_func_vars,
grad_norm_clipping) # update p Net parameters
# Create callable functions
# update P NET
train = U.function(inputs=obs_ph_n + state_ph_n + act_ph_n +
obs_next_n + obs_pred_n,
outputs=loss,
updates=[optimize_expr])
# return action and state
step = U.function(inputs=[obs_ph_n[p_index]] + [state_ph_n[p_index]] +
[obs_pred_n[p_index]],
outputs=[act_sample] + [state] + [gru_out])
p_values = U.function(inputs=[obs_ph_n[p_index]] +
[state_ph_n[p_index]] + [obs_pred_n[p_index]],
outputs=p)
# target network
target_p, target_gru_out, target_state = \
p_policy(obs_input, state_input, obs_pred_input,
int(act_pdtype_n[p_index].param_shape()[0]), scope="target_p_policy", num_units=num_units)
target_obs_pred = p_predict(act_input,
target_gru_out,
int(obs_input.shape[1]),
scope="target_p_predict",
num_units=num_units)
target_p_func_vars = U.scope_vars(U.absolute_scope_name("target_p_policy")) + \
U.scope_vars(U.absolute_scope_name("target_p_predict"))
# update the parameters θ'i = τθi + (1 − τ)θ'i
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_step = U.function(inputs=[obs_ph_n[p_index]] +
[state_ph_n[p_index]] + [obs_pred_n[p_index]],
outputs=[target_act_sample] + [target_state] +
[target_gru_out])
# return predicted obs
gru_temp = tf.placeholder(tf.float32, [None] + [num_units],
name='gru_out')
pred_temp = p_predict(act_input,
gru_temp,
int(obs_input.shape[1]),
scope="p_predict",
num_units=num_units)
predict = U.function(inputs=[act_ph_n[p_index]] + [gru_temp],
outputs=pred_temp)
target_pred_temp = p_predict(act_input,
gru_temp,
int(obs_input.shape[1]),
scope="target_p_predict",
num_units=num_units)
target_predict = U.function(inputs=[act_ph_n[p_index]] + [gru_temp],
outputs=target_pred_temp)
return step, predict, train, update_target_p, {
'p_values': p_values,
'target_step': target_step,
'target_predict': target_predict
}
def p_train(make_obs_ph_n,
act_space_n,
make_obs_history_n,
make_act_history_n,
p_index,
p_func,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
num_units=64,
scope="trainer",
reuse=None):
"""
Policy learning guided by Q-value
Args:
make_obs_ph_n (tf.placeholder): Placeholder for the observation space of all agents
act_space_n (list): A list of the action spaces for all agents
make_obs_history_n (tf.placeholder): Placeholder for the observation history of all agents
make_act_history_n (tf.placeholder): Placeholder for the action space history of all agents
p_index (int): Agent index number
p_func (function): MLP Neural Network model for the agent.
q_func (function): MLP Neural Network model for the agent.
optimizer (function): Network Optimizer function
grad_norm_clipping (float): Value by which to clip the norm of the gradient
local_q_func (boolean): Flag for using local q function
num_units (int): The number outputs for the layers of the model
scope (str): The name of the scope
reuse (boolean): Flag specifying whether to reuse the scope
Returns:
act (function): Action function for retrieving agent action.
train (function): Training function for P network
update_target_p (function): Update function for updating P network values
p_debug (dict): Contains 'p_values' and 'target_act' of the P network
"""
with tf.variable_scope(scope, reuse=reuse):
# 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
obs_history_n = make_obs_history_n
act_history_n = make_act_history_n
act_ph_n = [
act_pdtype_n[i].sample_placeholder([None], name="action" + str(i))
for i in range(len(act_space_n))
]
ccm_ph_n = [
tf.placeholder(tf.float32, [None], name="ccm" + str(p_index))
]
ccm_lambda = [
tf.placeholder(tf.float32, [None], name="lambda" + str(p_index))
]
ccm_switch = [
tf.placeholder(tf.float32, [None], name="switch" + str(p_index))
]
# Original implementation
# p_input = obs_ph_n[p_index]
# Modified
p_input = tf.concat([obs_ph_n[p_index], obs_history_n[p_index]], 1)
p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="p_func",
num_units=num_units)
p_func_vars = tf_util.scope_vars(tf_util.absolute_scope_name("p_func"))
# Wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
act_sample = act_pd.sample()
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
# Original implementation
act_input_n = act_ph_n + []
act_input_n[p_index] = act_pd.sample()
# Modified
# act_input_n = act_hist_ph_n + []
# act_input_n[p_index] = act_pd.mode()
# Original implementation
# q_input = tf.concat(obs_ph_n + act_input_n, 1)
# Modified
# Current plus previous time-steps
q_input = tf.concat(
obs_ph_n + obs_history_n + act_ph_n + act_history_n, 1)
if local_q_func:
# Only have observations about myself when 'ddpg'
# Importantly... [my.x, my.y, my.dx, my.dy, r1.x]
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input, 1, scope="q_func", reuse=True,
num_units=num_units)[:, 0]
# This is probably because of DDPG, rather than DSPG
# pg_loss = -tf.reduce_mean(q)
# ************************************************************************************************
# ccm_input = something
# ccm_value = ccm_func(ccm_input)
pg_loss = -(1 - ccm_lambda[0]) * tf.reduce_mean(q) * (1 - ccm_switch[0]) - \
ccm_lambda[0] * ccm_ph_n[0] * ccm_switch[0]
# ************************************************************************************************
loss = pg_loss + p_reg * 1e-3
optimize_expr = tf_util.minimize_and_clip(optimizer, loss, p_func_vars,
grad_norm_clipping)
# Create callable functions
train = tf_util.function(inputs=obs_ph_n + obs_history_n + act_ph_n +
act_history_n + ccm_ph_n + ccm_lambda +
ccm_switch,
outputs=loss,
updates=[optimize_expr])
# Original implementation
# act = tf_util.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
# p_values = tf_util.function([obs_ph_n[p_index]], p)
# Modified
act = tf_util.function(inputs=[obs_ph_n[p_index]] +
[obs_history_n[p_index]],
outputs=act_sample)
p_values = tf_util.function(inputs=[obs_ph_n[p_index]] +
[obs_history_n[p_index]],
outputs=p)
# target network
target_p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="target_p_func",
num_units=num_units)
target_p_func_vars = tf_util.scope_vars(
tf_util.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
# Original implementation
# target_act = tf_util.function(inputs=[obs_ph_n[p_index]], outputs=target_act_sample)
# Modified
target_act = tf_util.function(inputs=[obs_ph_n[p_index]] +
[obs_history_n[p_index]],
outputs=target_act_sample)
return act, train, update_target_p, {
'p_values': p_values,
'target_act': target_act
}
def p_train(make_obs_ph_n,
act_space_n,
p_index,
p_func,
q_func,
optimizer,
num_outputs,
grad_norm_clipping=None,
local_q_func=False,
num_units=64,
scope="coma_trainer",
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# 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(len(act_space_n))]
act_ph_n = [
tf.placeholder(tf.int32, [None], name="action" + str(i))
for i in range(len(act_space_n))
]
# actor的输入为本地的obs
p_input = obs_ph_n[p_index]
p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="coma_p_func",
num_units=num_units)
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# wrap parameters in distribution
act_pd = act_pdtype_n[p_index].pdfromflat(p)
# 得到各个action的概率
act_sample = act_pd.sample()
# sample操作即gumble softmax coma训练需要某个特定的动作,所以需要一个argmax操作
act_picked = [act.tolist().index(max(act)) for act in act_sample]
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
# 为什么要加一个[]
act_input_n = act_ph_n + []
# 动作概率分布 替换当前agent的动作
act_input_n[p_index] = act_picked
q_input = tf.concat(obs_ph_n + act_input_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[p_index], act_input_n[p_index]], 1)
q = q_func(q_input,
num_outputs,
scope="coma_q_func",
reuse=True,
num_units=num_units)
# 反事实基线
baseline = [
baseline_calculation(act_distribute, q_list)
for act_distribute, q_list in zip(act_sample, q)
]
# 根据真实采取的动作获得q
actual_picked_q = [q_list[act] for act, q_list in zip(act_picked, q)]
# 计算当前动作的q相对于反事实基线的差值
a = [q - b for q, b in zip(actual_picked_q, baseline)]
pg_loss = -tf.reduce_mean(a)
loss = pg_loss + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n,
outputs=loss,
updates=[optimize_expr])
act = U.function(inputs=[obs_ph_n[p_index]], outputs=act_sample)
p_values = U.function([obs_ph_n[p_index]], p)
# target network
target_p = p_func(p_input,
int(act_pdtype_n[p_index].param_shape()[0]),
scope="coma_target_p_func",
num_units=num_units)
target_p_func_vars = U.scope_vars(
U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[p_index].pdfromflat(target_p).sample()
target_act = U.function(inputs=[obs_ph_n[p_index]],
outputs=target_act_sample)
return act, train, update_target_p, {
'p_values': p_values,
'target_act': target_act
}