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[rllib] Remove need to pass around registry (#2250)

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* remove registry

* fix

* too many _

* fix

* cloudpickle

* Update registry.py

* yapf

* fix test

* fix kv check
This commit is contained in:
Eric Liang
2018-06-19 22:47:00 -07:00
committed by GitHub
parent 30684446a6
commit 30f7c08ca7
36 changed files with 202 additions and 208 deletions
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python/ray/rllib/ddpg/ddpg_policy_graph.py
+11 -15
View File
@@ -22,12 +22,12 @@ Q_SCOPE = "q_func"
Q_TARGET_SCOPE = "target_q_func"
def _build_p_network(registry, inputs, dim_actions, config):
def _build_p_network(inputs, dim_actions, config):
"""
map an observation (i.e., state) to an action where
each entry takes value from (0, 1) due to the sigmoid function
"""
frontend = ModelCatalog.get_model(registry, inputs, 1, config["model"])
frontend = ModelCatalog.get_model(inputs, 1, config["model"])
hiddens = config["actor_hiddens"]
action_out = frontend.last_layer
@@ -66,8 +66,8 @@ def _build_action_network(p_values, low_action, high_action, stochastic, eps,
lambda: deterministic_actions)
def _build_q_network(registry, inputs, action_inputs, config):
frontend = ModelCatalog.get_model(registry, inputs, 1, config["model"])
def _build_q_network(inputs, action_inputs, config):
frontend = ModelCatalog.get_model(inputs, 1, config["model"])
hiddens = config["critic_hiddens"]
@@ -81,7 +81,7 @@ def _build_q_network(registry, inputs, action_inputs, config):
class DDPGPolicyGraph(TFPolicyGraph):
def __init__(self, observation_space, action_space, registry, config):
def __init__(self, observation_space, action_space, config):
if not isinstance(action_space, Box):
raise UnsupportedSpaceException(
"Action space {} is not supported for DDPG.".format(
@@ -105,7 +105,7 @@ class DDPGPolicyGraph(TFPolicyGraph):
# Actor: P (policy) network
with tf.variable_scope(P_SCOPE) as scope:
p_values = _build_p_network(registry, self.cur_observations,
p_values = _build_p_network(self.cur_observations,
dim_actions, config)
self.p_func_vars = _scope_vars(scope.name)
@@ -136,13 +136,11 @@ class DDPGPolicyGraph(TFPolicyGraph):
# p network evaluation
with tf.variable_scope(P_SCOPE, reuse=True) as scope:
self.p_t = _build_p_network(
registry, self.obs_t, dim_actions, config)
self.p_t = _build_p_network(self.obs_t, dim_actions, config)
# target p network evaluation
with tf.variable_scope(P_TARGET_SCOPE) as scope:
p_tp1 = _build_p_network(
registry, self.obs_tp1, dim_actions, config)
p_tp1 = _build_p_network(self.obs_tp1, dim_actions, config)
target_p_func_vars = _scope_vars(scope.name)
# Action outputs
@@ -161,17 +159,15 @@ class DDPGPolicyGraph(TFPolicyGraph):
# q network evaluation
with tf.variable_scope(Q_SCOPE) as scope:
q_t = _build_q_network(
registry, self.obs_t, self.act_t, config)
q_t = _build_q_network(self.obs_t, self.act_t, config)
self.q_func_vars = _scope_vars(scope.name)
with tf.variable_scope(Q_SCOPE, reuse=True):
q_tp0 = _build_q_network(
registry, self.obs_t, output_actions, config)
q_tp0 = _build_q_network(self.obs_t, output_actions, config)
# target q network evalution
with tf.variable_scope(Q_TARGET_SCOPE) as scope:
q_tp1 = _build_q_network(
registry, self.obs_tp1, output_actions_estimated, config)
self.obs_tp1, output_actions_estimated, config)
target_q_func_vars = _scope_vars(scope.name)
q_t_selected = tf.squeeze(q_t, axis=len(q_t.shape) - 1)