def p_approx_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))]
act_logits_ph_n = [act_pdtype_n[i].sample_placeholder([None], name="action_mode" + 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")
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()))
p_reg = -tf.reduce_mean(act_pd.entropy())
act_input_n = act_ph_n + []
act_target = act_input_n[p_index]
pg_loss = -tf.reduce_mean(act_pd.logp(act_target))
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_pd = act_pdtype_n[p_index].pdfromflat(target_p)
target_act_sample = target_pd.sample()
target_act = U.function(inputs=[obs_ph_n[p_index]], outputs=target_act_sample)
target_ph_pd = act_pdtype_n[p_index].pdfromflat(act_logits_ph_n[p_index])
kl_loss = tf.reduce_mean(target_pd.kl(target_ph_pd))
f_kl_loss = U.function(inputs=[obs_ph_n[p_index],act_logits_ph_n[p_index]],outputs=kl_loss)
return act, train, update_target_p, sync_target_p, {'p_values': p_values, 'kl_loss': f_kl_loss, '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=tf.AUTO_REUSE):
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)
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)
act_test = U.function(inputs=[obs_ph_n[p_index]], outputs=p)
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_test, act, train, update_target_p, {'p_values': p_values, 'target_act': target_act,
'p_vars': p_func_vars, 'target_p_vars': target_p_func_vars}
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 q_train(make_obs_ph_n, act_space_n, q_index, q_func, shared_CNN, optimizer, make_obs_map_ph_n,grad_norm_clipping=None, local_q_func=False, scope="trainer", reuse=None, num_units=64):
# 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_map_ph_n=make_obs_map_ph_n
obs_ph_next = 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")
vf_input = tf.concat(obs_ph_next, 1)
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
for i in range(len(obs_ph_n)):
q_input=tf.concat([q_input,shared_CNN(obs_map_ph_n[i],i,scope="agent_"+str(i)+"/CNN")],1)
vf_input=tf.concat([vf_input,shared_CNN(obs_map_ph_n[i],i,scope="agent_"+str(i)+"/CNN")],1)
with tf.variable_scope(scope, reuse=None):
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]
vf = q_func(vf_input, 1, scope="vf_func",num_units=num_units)[:,0]
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
vf_func_vars = U.scope_vars(U.absolute_scope_name("vf_func"))
CNN_vars=U.scope_vars(U.absolute_scope_name("CNN"))
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+CNN_vars, grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + obs_map_ph_n+act_ph_n + [target_ph], outputs=loss, updates=[optimize_expr])
q_values = U.function(obs_ph_n + obs_map_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 + obs_map_ph_n+act_ph_n, target_q)
target_vf_values = U.function(obs_ph_n +obs_map_ph_n, vf)
return train, update_target_q, {'q_values': q_values, 'target_vf_values': target_vf_values}
def q_LSTM_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, 1], name="action"+str(i)) for i in range(len(act_space_n))]
target_ph = tf.placeholder(tf.float32, [None, 1], name="target")
q_res = 1
q_c_ph, q_h_ph = get_lstm_state_ph(name='q_', n_batches=None, num_units=num_units)
q_c_ph_n, q_h_ph_n = [q_c_ph for i in range(len(obs_ph_n))], [q_h_ph for i in range(len(obs_ph_n))]
# need to check this -- need safety checks
# q_input = tf.concat(obs_ph_n + act_ph_n, -1)
q_input = tf.concat(obs_ph_n + act_ph_n + q_c_ph_n + q_h_ph_n, -1)
if local_q_func:
q_input = tf.concat([obs_ph_n[q_index], act_ph_n[q_index]], -1)
q, q_state_out = q_func(q_input, 1, scope="q_func", num_units=num_units)
q = q[:,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
q_values = U.function(inputs=obs_ph_n + act_ph_n + q_c_ph_n + q_h_ph_n, outputs=[q, q_state_out])
train = U.function(inputs=obs_ph_n + act_ph_n + q_c_ph_n + q_h_ph_n+ [target_ph], outputs=loss, updates=[optimize_expr])
# target network
target_q, target_q_state_out = q_func(q_input, 1, scope="target_q_func", num_units=num_units)
target_q = target_q[:,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(inputs=obs_ph_n + act_ph_n + q_c_ph_n + q_h_ph_n, outputs=target_q)
return train, update_target_q, {'q_values': q_values, 'target_q_values': target_q_values}
def make_update_exp(vals, target_vals):
polyak = 1.0 - 1e-2
expression = []
for var, var_target in zip(sorted(vals, key=lambda v: v.name), sorted(target_vals, key=lambda v: v.name)):
expression.append(var_target.assign(polyak * var_target + (1.0-polyak) * var))
expression = tf.group(*expression)
return U.function([], [], updates=[expression])
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 make_update_exp(vals, target_vals): # softupdate两个神经网络的变量
polyak = 1.0 - 1e-2 # 0.99
expression = []
for var, var_target in zip(sorted(vals, key=lambda v: v.name),
sorted(target_vals, key=lambda v: v.name)):
expression.append(
var_target.assign(polyak * var_target +
(1.0 - polyak) * var)) # target网络和当前网络
expression = tf.group(*expression) # expression被会话调用，其中所有变量均会被调用生效
return U.function([], [], updates=[expression]) # 更新target_vals网络节点的值
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 # [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))]
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))
# q_loss = tf.reduce_mean(U.huber_loss(q - target_ph))
# TEMP: just want to give an 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)
sync_target_q = make_update_exp(q_func_vars, target_q_func_vars, rate=1.0)
target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
return train, update_target_q, sync_target_q, {'q_values': q_values, 'target_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
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) # DDPG just owns its [local] information
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 # From chapter 4.2: inferring policies of other policies ???
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars, grad_norm_clipping) # [Important]使用 optimizer 来降低 loss, 其中变量表在 q_func_vars 中,
# 保证每个变量的梯度到 grad_norm_clipping -- 梯度剪切
# 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 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 = make_pdtype(act_space_n[0])
# set up placeholders
obs_ph_n = make_obs_ph_n
act_ph = [act_pdtype.sample_placeholder([None], name="action"+str(0))]
target_ph = tf.placeholder(tf.float32, [None], name="target")
q_input = tf.concat(obs_ph_n + act_ph, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[q_index], act_ph[0]], 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 + [target_ph], outputs=loss, updates=[optimize_expr])
q_values = U.function(obs_ph_n + act_ph, 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, target_q)
return train, update_target_q, {'q_values': q_values, 'target_q_values': target_q_values}
def __init__(self, name, model, obs_shape_n, act_space_n, agent_index,
args):
self.name = name
self.n = len(obs_shape_n)
self.agent_index = agent_index
self.args = args
# create dummy tensor flow variables to avoid Saver error
# TODO: remove this or turn into act function
obs_ph_n = []
for i in range(self.n):
obs_ph_n.append(
U.BatchInput(obs_shape_n[i],
name="observation" + str(i)).get())
with tf.variable_scope(self.name, reuse=None):
self.dummy_var = U.function(obs_ph_n, outputs=tf.Variable(0))
def make_update_exp(vals, target_vals, central=True):
polyak = 1.0 - 1e-2
expression = []
agent_n_species = len(vals)
if central:
for var, var_target in zip(sorted(vals, key=lambda v: v.name),
sorted(target_vals, key=lambda v: v.name)):
expression.append(
var_target.assign(polyak * var_target + (1.0 - polyak) * var))
else:
for a in range(agent_n_species):
for var, var_target in zip(
sorted(vals[a], key=lambda v: v.name),
sorted(target_vals[a], key=lambda v: v.name)):
expression.append(
var_target.assign(polyak * var_target +
(1.0 - polyak) * var))
expression = tf.group(*expression)
return U.function([], [], updates=[expression])
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 make_update_exp(vals, target_vals):
"""
Update target network values using polyak averaging (exponentially decaying average).
Args:
vals (tf.Variable): Network variables
target_vals (tf.Variable): Target network variables
Returns
Updated target network values
"""
# Polyak coefficient for Polyak-averaging of the target network
polyak = 1.0 - 1e-2
expression = []
for var, var_target in zip(sorted(vals, key=lambda v: v.name),
sorted(target_vals, key=lambda v: v.name)):
# Exponentially decaying average
expression.append(
var_target.assign(polyak * var_target + (1.0 - polyak) * var))
expression = tf.group(*expression)
return tf_util.function([], [], updates=[expression])
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(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 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 q_train(make_obs_ph_n,
act_space_n,
q_index,
q_func,
optimizer,
grad_norm_clipping=None,
scope="coma_trainer",
reuse=None,
num_units=64,
num_outputs=1):
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 = [
tf.placeholder(tf.int32, [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(len(act_space_n))
]
# 在一维进行拼接
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
q = q_func(q_input,
num_outputs,
scope="coma_q_func",
num_units=num_units)
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
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,
num_outputs,
scope="coma_target_q_func",
num_units=num_units)
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 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 p_train(env, make_obs_ph_n, act_space_n, p_index, vf_func, shana, q_func, shared_CNN, optimizer, make_obs_map_ph_n,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))]
obs_map_ph_n=make_obs_map_ph_n
p_map_input=obs_map_ph_n[p_index]
policy = shana(
env_spec=env,
af = 15,
of = 25+12,
K=2,
hidden_layer_sizes=(128, 128),
qf=q_func,
reg=0.001
)
map_context_input=shared_CNN(p_map_input,p_index,scope="CNN")
CNN_vars=U.scope_vars(U.absolute_scope_name("CNN"))
act, log_pi = policy.actions_for(observations=tf.concat([make_obs_ph_n[p_index],map_context_input],1),
with_log_pis=True)
act_input_n = act_ph_n + []
act_input_n[p_index]=act
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)
for i in range(len(obs_ph_n)):
q_input=tf.concat([q_input,shared_CNN(obs_map_ph_n[i],i,scope="agent_"+str(i)+"/CNN")],1)
vf_input=tf.concat([vf_input,shared_CNN(obs_map_ph_n[i],i,scope="agent_"+str(i)+"/CNN")],1)
with tf.variable_scope(scope, reuse=None):
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)
with tf.variable_scope(scope, reuse=True):
CNN_p_optimizer=U.minimize_and_clip(optimizer, loss, CNN_vars, grad_norm_clipping)
CNN_v_optimizaer=U.minimize_and_clip(optimizer, vf_loss, CNN_vars, grad_norm_clipping)
with tf.variable_scope(scope, reuse=None):
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n+obs_map_ph_n, outputs=loss, updates=[optimize_expr,CNN_p_optimizer])
misaka = U.function(inputs=obs_ph_n + act_ph_n+obs_map_ph_n, outputs=loss, updates=[mikoto,CNN_v_optimizaer])
# target network
target_p = shana(
env_spec=env,
af = 15,
of = 25+12,
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=tf.concat([obs_ph_n[p_index],map_context_input],1),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], obs_map_ph_n[p_index]], outputs=target_act_r)
act=U.function(inputs=[obs_ph_n[p_index], obs_map_ph_n[p_index]], outputs=act)
return act, train, misaka, update_target_p, upvf, {'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
}
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 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(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 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 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 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 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
}
