def build_graph(self, graph):
with graph.as_default():
tf.set_random_seed(self.random_seed)
self.inputs_plh = tf.placeholder(tf.int32, shape=[None], name="inputs_plh")
q_scope = tf.VariableScope(reuse=False, name='QValues')
with tf.variable_scope(q_scope):
self.Qs = tf.get_variable('Qs'
, shape=[self.nb_state, self.action_space.n]
, initializer=tf.constant_initializer(self.initial_q_value)
, dtype=tf.float32
)
tf.summary.histogram('Qarray', self.Qs)
self.q_preds_t = tf.gather(self.Qs, self.inputs_plh)
policy_scope = tf.VariableScope(reuse=False, name='Policy')
with tf.variable_scope(policy_scope):
if 'UCB' in self.config and self.config['UCB']:
self.actions_t, self.probs_t = capacities.tabular_UCB(
self.Qs, self.inputs_plh
)
else:
self.actions_t, self.probs_t = capacities.tabular_eps_greedy(
self.inputs_plh, self.q_preds_t, self.nb_state, self.env.action_space.n, self.N0, self.min_eps
)
self.action_t = self.actions_t[0]
self.q_value_t = self.q_preds_t[0][self.action_t]
learning_scope = tf.VariableScope(reuse=False, name='Learning')
with tf.variable_scope(learning_scope):
self.rewards_plh = tf.placeholder(tf.float32, shape=[None], name="rewards_plh")
self.next_states_plh = tf.placeholder(tf.int32, shape=[None], name="next_states_plh")
self.next_actions_plh = tf.placeholder(tf.int32, shape=[None], name="next_actions_plh")
targets_t = capacities.get_td_target(self.Qs, self.rewards_plh, self.next_states_plh, self.next_actions_plh, self.discount)
# When boostraping, the target is non-stationnary, we need a learning rate
self.loss, self.train_op = capacities.tabular_learning_with_lr(
self.lr, self.lr_decay_steps, self.Qs, self.inputs_plh, self.actions_t, targets_t
)
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):
with graph.as_default():
tf.set_random_seed(self.random_seed)
self.inputs_plh = tf.placeholder(tf.int32,
shape=[None],
name="inputs_plh")
q_scope = tf.VariableScope(reuse=False, name='QValues')
with tf.variable_scope(q_scope):
self.Qs = tf.get_variable(
'Qs',
shape=[self.nb_state, self.action_space.n],
initializer=tf.constant_initializer(self.initial_q_value),
dtype=tf.float32)
tf.summary.histogram('Qarray', self.Qs)
self.q_preds_t = tf.gather(self.Qs, self.inputs_plh)
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)
policy_scope = tf.VariableScope(reuse=False, name='Policy')
with tf.variable_scope(policy_scope):
if 'UCB' in self.config and self.config['UCB']:
self.actions_t, self.probs_t = capacities.tabular_UCB(
self.Qs, self.inputs_plh)
else:
self.actions_t, self.probs_t = capacities.tabular_eps_greedy(
self.inputs_plh, self.q_preds_t, self.nb_state,
self.env.action_space.n, self.N0, self.min_eps)
self.action_t = self.actions_t[0]
self.q_value_t = self.q_preds_t[0][self.action_t]
# Experienced replay part
with tf.variable_scope('Learning'):
with tf.variable_scope(fixed_q_scope, reuse=True):
fixed_Qs = tf.get_variable('Qs')
self.rewards_plh = tf.placeholder(tf.float32,
shape=[None],
name="rewards_plh")
self.next_states_plh = tf.placeholder(tf.int32,
shape=[None],
name="next_states_plh")
# Note that we use the fixed Qs to create the targets
self.targets_t = capacities.get_q_learning_target(
fixed_Qs, self.rewards_plh, self.next_states_plh,
self.discount)
self.loss, self.train_op = capacities.tabular_learning_with_lr(
self.lr, self.lr_decay_steps, self.Qs, self.inputs_plh,
self.actions_t, self.targets_t)
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.event_count, self.inc_event_count_op = capacities.counter(
"event_count")
# 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_plh = tf.placeholder(tf.int32,
shape=[None],
name="inputs_plh")
q_scope = tf.VariableScope(reuse=False, name='QValues')
with tf.variable_scope(q_scope):
self.Qs = tf.get_variable(
'Qs',
shape=[self.nb_state, self.action_space.n],
initializer=tf.constant_initializer(self.initial_q_value),
dtype=tf.float32)
tf.summary.histogram('Qarray', self.Qs)
self.q_preds_t = tf.gather(self.Qs, self.inputs_plh)
policy_scope = tf.VariableScope(reuse=False, name='Policy')
with tf.variable_scope(policy_scope):
if 'UCB' in self.config and self.config['UCB']:
self.actions_t, self.probs_t = capacities.tabular_UCB(
self.Qs, self.inputs_plh)
else:
self.actions_t, self.probs_t = capacities.tabular_eps_greedy(
self.inputs_plh, self.q_preds_t, self.nb_state,
self.env.action_space.n, self.N0, self.min_eps)
self.action_t = self.actions_t[0]
self.q_value_t = self.q_preds_t[0][self.action_t]
et_scope = tf.VariableScope(reuse=False, name='EligibilityTraces')
with tf.variable_scope(et_scope):
et, update_et_op, self.reset_et_op = capacities.eligibility_traces(
self.Qs, self.inputs_plh, self.actions_t, self.discount,
self.lambda_value)
with tf.variable_scope('Learning'):
self.rewards_plh = tf.placeholder(tf.float32,
shape=[None],
name="rewards_plh")
self.next_states_plh = tf.placeholder(tf.int32,
shape=[None],
name="next_states_plh")
self.targets_t = capacities.get_q_learning_target(
self.Qs, self.rewards_plh, self.next_states_plh,
self.discount)
target = self.targets_t[0]
state_action_pairs = tf.stack(
[self.inputs_plh, self.actions_t], 1)
estimate = tf.gather_nd(self.Qs, state_action_pairs)[0]
err_estimate = target - estimate
global_step = tf.Variable(0,
trainable=False,
name="global_step",
collections=[
tf.GraphKeys.GLOBAL_STEP,
tf.GraphKeys.GLOBAL_VARIABLES
])
lr = tf.train.exponential_decay(tf.constant(self.lr,
dtype=tf.float32),
global_step,
self.lr_decay_steps,
0.5,
staircase=True)
tf.summary.scalar('lr', lr)
inc_global_step = global_step.assign_add(1)
with tf.control_dependencies([update_et_op, inc_global_step]):
self.loss = tf.reduce_sum(err_estimate * et)
self.train_op = tf.assign_add(self.Qs,
lr * err_estimate * et)
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