def build_graph(self, graph):
with graph.as_default():
tf.set_random_seed(self.random_seed)
self.inputs = tf.placeholder(
tf.float32,
shape=[None, self.observation_space.shape[0] + 1],
name='inputs')
policy_scope = tf.VariableScope(reuse=False, name='Policy')
with tf.variable_scope(policy_scope):
self.probs, self.actions = capacities.policy(
self.policy_params, self.inputs)
self.action_t = tf.squeeze(self.actions, 1)[0]
# self.action_t = tf.Print(self.action_t, data=[self.probs, self.action_t], message="self.probs, self.action_t:")
v_scope = tf.VariableScope(reuse=False, name='VValues')
with tf.variable_scope(v_scope):
vs = capacities.value_f(self.v_params, self.inputs)
with tf.control_dependencies([self.probs, vs]):
with tf.variable_scope('Training'):
stacked_actions = tf.stack([
tf.range(0,
tf.shape(self.actions)[0]),
tf.squeeze(self.actions, 1)
], 1)
self.rewards = tf.placeholder(tf.float32,
shape=[None],
name="rewards")
self.next_states = tf.placeholder(
tf.float32,
shape=[None, self.observation_space.shape[0] + 1],
name="next_states")
self.next_actions = tf.placeholder(tf.int32,
shape=[None],
name="next_actions")
with tf.variable_scope(v_scope, reuse=True):
next_vs = tf.squeeze(
capacities.value_f(self.v_params,
self.next_states), 1)
with tf.variable_scope('TargetVs'):
target_vs1 = tf.stop_gradient(self.rewards +
self.discount * next_vs)
target_vs2 = self.rewards
stacked_targets = tf.stack([target_vs1, target_vs2], 1)
select_targets = tf.stack([
tf.range(0,
tf.shape(self.next_states)[0]),
tf.cast(self.next_states[:, -1], tf.int32)
], 1)
target_vs = tf.gather_nd(stacked_targets,
select_targets)
log_probs = tf.log(
tf.gather_nd(self.probs, stacked_actions))
with tf.control_dependencies([log_probs, target_vs]):
self.v_loss = 1 / 2 * tf.reduce_sum(
tf.square(target_vs - vs))
v_adam = tf.train.AdamOptimizer(self.v_lr)
self.v_global_step = tf.Variable(0,
trainable=False,
name="v_global_step")
self.v_train_op = v_adam.minimize(
self.v_loss, global_step=self.v_global_step)
td = target_vs - vs
self.policy_loss = -tf.reduce_sum(
log_probs * tf.stop_gradient(td))
policy_adam = tf.train.AdamOptimizer(self.policy_lr)
self.policy_global_step = tf.Variable(
0,
trainable=False,
name="policy_global_step",
collections=[
tf.GraphKeys.GLOBAL_STEP,
tf.GraphKeys.GLOBAL_VARIABLES
])
self.policy_train_op = policy_adam.minimize(
self.policy_loss,
global_step=self.policy_global_step)
self.score_plh = tf.placeholder(tf.float32, shape=[])
self.score_sum_t = tf.summary.scalar('score', self.score_plh)
self.policy_loss_plh = tf.placeholder(tf.float32, shape=[])
self.policy_loss_sum_t = tf.summary.scalar('policy_loss',
self.policy_loss_plh)
self.v_loss_plh = tf.placeholder(tf.float32, shape=[])
self.v_loss_sum_t = tf.summary.scalar('v_loss', self.v_loss_plh)
self.all_summary_t = tf.summary.merge_all()
self.episode_id, self.inc_ep_id_op = capacities.counter(
"episode_id")
# Playing part
self.pscore_plh = tf.placeholder(tf.float32, shape=[])
self.pscore_sum_t = tf.summary.scalar('play_score',
self.pscore_plh)
return graph
def build_graph(self, graph):
with graph.as_default():
tf.set_random_seed(self.random_seed)
self.inputs = tf.placeholder(
tf.float32,
shape=[None, self.observation_space.shape[0] + 1],
name='inputs')
policy_scope = tf.VariableScope(reuse=False, name='Policy')
with tf.variable_scope(policy_scope):
self.probs, self.actions = capacities.policy(
self.policy_params, self.inputs)
self.action_t = tf.squeeze(self.actions, 1)[0]
q_scope = tf.VariableScope(reuse=False, name='QValues')
with tf.variable_scope(q_scope):
self.q_values = capacities.value_f(self.q_params, self.inputs)
self.q = self.q_values[0, tf.stop_gradient(self.action_t)]
with tf.variable_scope('Training'):
stacked_actions = tf.stack([
tf.range(0,
tf.shape(self.actions)[0]),
tf.squeeze(self.actions, 1)
], 1)
qs = tf.gather_nd(self.q_values, stacked_actions)
log_probs = tf.log(tf.gather_nd(self.probs, stacked_actions))
self.policy_loss = -tf.reduce_sum(
log_probs * tf.stop_gradient(qs))
self.rewards = tf.placeholder(tf.float32,
shape=[None],
name="rewards")
self.next_states = tf.placeholder(
tf.float32,
shape=[None, self.observation_space.shape[0] + 1],
name="next_states")
self.next_actions = tf.placeholder(tf.int32,
shape=[None],
name="next_actions")
with tf.variable_scope(q_scope, reuse=True):
next_q_values = capacities.value_f(self.q_params,
self.next_states)
next_stacked_actions = tf.stack([
tf.range(0,
tf.shape(self.next_actions)[0]), self.next_actions
], 1)
next_qs = tf.gather_nd(next_q_values, next_stacked_actions)
target_qs1 = tf.stop_gradient(self.rewards +
self.discount * next_qs)
target_qs2 = self.rewards
stacked_targets = tf.stack([target_qs1, target_qs2], 1)
select_targets = tf.stack([
tf.range(0,
tf.shape(self.next_states)[0]),
tf.cast(self.next_states[:, -1], tf.int32)
], 1)
target_qs = tf.gather_nd(stacked_targets, select_targets)
self.q_loss = 1 / 2 * tf.reduce_sum(tf.square(target_qs - qs))
self.loss = self.policy_loss + self.q_scale_lr * self.q_loss
adam = tf.train.AdamOptimizer(self.lr)
self.global_step = tf.Variable(
0,
trainable=False,
name="global_step",
collections=[
tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES
])
self.train_op = adam.minimize(self.loss,
global_step=self.global_step)
self.score_plh = tf.placeholder(tf.float32, shape=[])
self.score_sum_t = tf.summary.scalar('score', self.score_plh)
self.policy_loss_plh = tf.placeholder(tf.float32, shape=[])
self.policy_loss_sum_t = tf.summary.scalar('policy_loss',
self.policy_loss_plh)
self.q_loss_plh = tf.placeholder(tf.float32, shape=[])
self.q_loss_sum_t = tf.summary.scalar('q_loss', self.q_loss_plh)
self.loss_plh = tf.placeholder(tf.float32, shape=[])
self.loss_sum_t = tf.summary.scalar('loss', self.loss_plh)
self.all_summary_t = tf.summary.merge_all()
self.episode_id, self.inc_ep_id_op = capacities.counter(
"episode_id")
# Playing part
self.pscore_plh = tf.placeholder(tf.float32, shape=[])
self.pscore_sum_t = tf.summary.scalar('play_score',
self.pscore_plh)
return graph
def build_graph(self, graph):
with graph.as_default():
tf.set_random_seed(self.random_seed)
self.inputs = tf.placeholder(
tf.float32,
shape=[None, self.observation_space.shape[0] + 1],
name='inputs')
q_scope = tf.VariableScope(reuse=False, name='QValues')
with tf.variable_scope(q_scope):
self.q_values = tf.squeeze(
capacities.value_f(self.q_params, self.inputs))
self.action_t = capacities.eps_greedy(self.inputs, self.q_values,
self.env.action_space.n,
self.N0, self.min_eps)
self.q_t = self.q_values[self.action_t]
fixed_q_scope = tf.VariableScope(reuse=False, name='FixedQValues')
with tf.variable_scope(fixed_q_scope):
self.update_fixed_vars_op = capacities.fix_scope(q_scope)
with tf.variable_scope('ExperienceReplay'):
self.er_inputs = tf.placeholder(
tf.float32,
shape=[None, self.observation_space.shape[0] + 1],
name="ERInputs")
self.er_actions = tf.placeholder(tf.int32,
shape=[None],
name="ERInputs")
self.er_rewards = tf.placeholder(tf.float32,
shape=[None],
name="ERReward")
self.er_next_states = tf.placeholder(
tf.float32,
shape=[None, self.observation_space.shape[0] + 1],
name="ERNextState")
with tf.variable_scope(q_scope, reuse=True):
er_q_values = capacities.value_f(self.q_params,
self.er_inputs)
er_stacked_actions = tf.stack([
tf.range(0,
tf.shape(self.er_actions)[0]), self.er_actions
], 1)
er_qs = tf.gather_nd(er_q_values, er_stacked_actions)
with tf.variable_scope(fixed_q_scope, reuse=True):
er_fixed_next_q_values = capacities.value_f(
self.q_params, self.er_next_states)
with tf.variable_scope(q_scope, reuse=True):
er_next_q_values = capacities.value_f(
self.q_params, self.er_next_states)
er_next_max_action_t = tf.cast(tf.argmax(er_next_q_values, 1),
tf.int32)
er_next_stacked_actions = tf.stack([
tf.range(0,
tf.shape(self.er_next_states)[0]),
er_next_max_action_t
], 1)
er_next_qs = tf.gather_nd(er_fixed_next_q_values,
er_next_stacked_actions)
er_target_qs1 = tf.stop_gradient(self.er_rewards +
self.discount * er_next_qs)
er_target_qs2 = self.er_rewards
er_stacked_targets = tf.stack([er_target_qs1, er_target_qs2],
1)
select_targets = tf.stack([
tf.range(0,
tf.shape(self.er_next_states)[0]),
tf.cast(self.er_next_states[:, -1], tf.int32)
], 1)
er_target_qs = tf.gather_nd(er_stacked_targets, select_targets)
self.er_loss = 1 / 2 * tf.reduce_sum(
tf.square(er_target_qs - er_qs))
er_adam = tf.train.AdamOptimizer(self.lr)
self.global_step = tf.Variable(
0,
trainable=False,
name="global_step",
collections=[
tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES
])
self.er_train_op = er_adam.minimize(
self.er_loss, global_step=self.global_step)
self.score_plh = tf.placeholder(tf.float32, shape=[])
self.score_sum_t = tf.summary.scalar('score', self.score_plh)
self.loss_plh = tf.placeholder(tf.float32, shape=[])
self.loss_sum_t = tf.summary.scalar('loss', self.loss_plh)
self.all_summary_t = tf.summary.merge_all()
self.episode_id, self.inc_ep_id_op = capacities.counter(
"episode_id")
self.timestep, self.inc_timestep_op = capacities.counter(
"timestep")
# Playing part
self.pscore_plh = tf.placeholder(tf.float32, shape=[])
self.pscore_sum_t = tf.summary.scalar('play_score',
self.pscore_plh)
return graph
def build_graph(self, graph):
with graph.as_default():
tf.set_random_seed(self.random_seed)
self.N0_t = tf.constant(self.N0, tf.float32, name='N_0')
self.N = tf.Variable(0.,
dtype=tf.float32,
name='N',
trainable=False)
self.min_eps_t = tf.constant(self.min_eps,
tf.float32,
name='min_eps')
self.inputs = tf.placeholder(
tf.float32,
shape=[None, self.observation_space.shape[0] + 1],
name='inputs')
q_scope = tf.VariableScope(reuse=False, name='QValues')
with tf.variable_scope(q_scope):
self.q_values = tf.squeeze(
capacities.value_f(self.q_params, self.inputs))
self.action_t = capacities.eps_greedy(self.inputs, self.q_values,
self.env.action_space.n,
self.N0, self.min_eps)
self.q_t = self.q_values[self.action_t]
with tf.variable_scope('Training'):
self.reward = tf.placeholder(tf.float32,
shape=[],
name="reward")
self.next_state = tf.placeholder(
tf.float32,
shape=[1, self.observation_space.shape[0] + 1],
name="nextState")
self.next_action = tf.placeholder(tf.int32,
shape=[],
name="nextAction")
with tf.variable_scope(q_scope, reuse=True):
next_q_values = tf.squeeze(
capacities.value_f(self.q_params, self.next_state))
target_q1 = tf.stop_gradient(self.reward + self.discount *
next_q_values[self.next_action])
target_q2 = self.reward
is_done = tf.cast(self.next_state[0, 4], tf.bool)
target_q = tf.where(is_done, target_q2, target_q1)
with tf.control_dependencies([target_q]):
self.loss = 1 / 2 * tf.square(target_q - self.q_t)
adam = tf.train.AdamOptimizer(self.lr)
self.global_step = tf.Variable(
0,
trainable=False,
name="global_step",
collections=[
tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES
])
self.train_op = adam.minimize(self.loss,
global_step=self.global_step)
self.score_plh = tf.placeholder(tf.float32, shape=[])
self.score_sum_t = tf.summary.scalar('score', self.score_plh)
self.loss_plh = tf.placeholder(tf.float32, shape=[])
self.loss_sum_t = tf.summary.scalar('loss', self.loss_plh)
self.all_summary_t = tf.summary.merge_all()
self.episode_id, self.inc_ep_id_op = capacities.counter(
"episode_id")
# Playing part
self.pscore_plh = tf.placeholder(tf.float32, shape=[])
self.pscore_sum_t = tf.summary.scalar('play_score',
self.pscore_plh)
return graph
def build_graph(self, graph):
np.random.seed(self.random_seed)
with graph.as_default():
tf.set_random_seed(self.random_seed)
self.inputs = tf.placeholder(tf.float32, shape=[None, None, self.policy_params['nb_inputs']], name='inputs')
input_shape = tf.shape(self.inputs)
dynamic_batch_size, dynamic_num_steps = input_shape[0], input_shape[1]
inputs_mat = tf.reshape(self.inputs, [-1, self.policy_params['nb_inputs']])
policy_scope = tf.VariableScope(reuse=False, name='Policy')
with tf.variable_scope(policy_scope):
probs, actions = capacities.policy(self.policy_params, inputs_mat)
self.probs = tf.reshape(probs, [dynamic_batch_size, dynamic_num_steps, self.policy_params['nb_outputs']])
self.actions = tf.reshape(actions, [dynamic_batch_size, dynamic_num_steps, 1])
self.action_t = self.actions[0, 0, 0]
critic_scope = tf.VariableScope(reuse=False, name='QValues')
with tf.variable_scope(critic_scope):
critic_values_mat = capacities.value_f(self.critic_params, inputs_mat)
self.critic_values = tf.reshape(critic_values_mat, [dynamic_batch_size, dynamic_num_steps, self.critic_params['nb_outputs']])
fixed_critic_scope = tf.VariableScope(reuse=False, name='FixedQValues')
with tf.variable_scope(fixed_critic_scope):
self.update_fixed_vars_op = capacities.fix_scope(critic_scope)
with tf.variable_scope('Training'):
self.expected_rewards = tf.placeholder(tf.float32, shape=[None, None, 1], name="reward")
self.mask_plh = tf.placeholder(tf.float32, shape=[None, None, 1], name="mask_plh")
batch_size, num_steps = tf.shape(self.actions)[0], tf.shape(self.actions)[1]
line_indices = tf.matmul( # Line indice
tf.reshape(tf.range(0, batch_size), [-1, 1])
, tf.ones([1, num_steps], dtype=tf.int32)
)
column_indices = tf.matmul( # Column indice
tf.ones([batch_size, 1], dtype=tf.int32)
, tf.reshape(tf.range(0, num_steps), [1, -1])
)
depth_indices = tf.cast(tf.squeeze(self.actions, 2), tf.int32)
stacked_actions = tf.stack(
[line_indices, column_indices, depth_indices], 2
)
log_probs = tf.expand_dims(tf.log(tf.gather_nd(self.probs, stacked_actions)), 2)
self.policy_loss = tf.reduce_mean( - tf.reduce_sum((log_probs * (self.expected_rewards - tf.stop_gradient(self.critic_values))) * self.mask_plh, 1))
adam = tf.train.AdamOptimizer(self.lr)
self.train_policy_op = adam.minimize(self.policy_loss)
self.rewards = tf.placeholder(tf.float32, shape=[None, None, 1], name="reward")
self.next_states = tf.placeholder(tf.float32, shape=[None, None, self.critic_params['nb_inputs']], name="next_states")
with tf.variable_scope(fixed_critic_scope, reuse=True):
next_states_mat = tf.reshape(self.next_states, [-1, self.critic_params['nb_inputs']])
next_critic_values_mat = capacities.value_f(self.critic_params, next_states_mat)
next_critic_values = tf.reshape(next_critic_values_mat, [dynamic_batch_size, dynamic_num_steps, self.critic_params['nb_outputs']])
target_critics1 = tf.stop_gradient(self.rewards + self.discount * next_critic_values)
target_critics2 = self.rewards
stacked_targets = tf.stack([tf.squeeze(target_critics1, 2), tf.squeeze(target_critics2, 2)], 2)
batch_size, num_steps = tf.shape(self.next_states)[0], tf.shape(self.next_states)[1]
line_indices = tf.matmul( # Line indice
tf.reshape(tf.range(0, batch_size), [-1, 1])
, tf.ones([1, num_steps], dtype=tf.int32)
)
column_indices = tf.matmul( # Column indice
tf.ones([batch_size, 1], dtype=tf.int32)
, tf.reshape(tf.range(0, num_steps), [1, -1])
)
depth_indices = tf.cast(self.next_states[:, :, -1], tf.int32)
select_targets = tf.stack(
[line_indices, column_indices, depth_indices], 2
)
target_critics = tf.expand_dims(tf.gather_nd(stacked_targets, select_targets), 2)
self.critic_loss = 1/2 * tf.reduce_sum(tf.square(target_critics - self.critic_values) * self.mask_plh)
adam = tf.train.AdamOptimizer(self.critic_lr)
self.global_step = tf.Variable(0, trainable=False, name="global_step", collections=[tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES])
self.train_critic_op = adam.minimize(self.critic_loss, global_step=self.global_step)
self.policy_loss_plh = tf.placeholder(tf.float32, shape=[])
self.policy_loss_sum_t = tf.summary.scalar('policy_loss', self.policy_loss_plh)
self.critic_loss_plh = tf.placeholder(tf.float32, shape=[])
self.critic_loss_sum_t = tf.summary.scalar('critic_loss', self.critic_loss_plh)
# self.loss_plh = tf.placeholder(tf.float32, shape=[])
# self.loss_sum_t = tf.summary.scalar('loss', self.loss_plh)
self.all_summary_t = tf.summary.merge_all()
self.score_plh = tf.placeholder(tf.float32, shape=[])
self.score_sum_t = tf.summary.scalar('av_score', self.score_plh)
self.episode_id, self.inc_ep_id_op = capacities.counter("episode_id")
# Playing part
self.pscore_plh = tf.placeholder(tf.float32, shape=[])
self.pscore_sum_t = tf.summary.scalar('play_score', self.pscore_plh)
return graph
