Temperature

tensor2tensor/models/research/rl.py

@@ -27,6 +27,7 @@
 from tensor2tensor.rl.envs.simulated_batch_env import SimulatedBatchEnv
 from tensor2tensor.rl.envs.simulated_batch_gym_env import SimulatedBatchGymEnv
 from tensor2tensor.utils import registry
+from tensor2tensor.utils import t2t_model
 
 import tensorflow as tf
 import tensorflow_probability as tfp
@@ -60,12 +61,12 @@ def ppo_base_v1():
   return hparams
 
 
-@registry.register_hparams
-def ppo_continuous_action_base():
-  hparams = ppo_base_v1()
-  hparams.add_hparam("policy_network", feed_forward_gaussian_fun)
-  hparams.add_hparam("policy_network_params", "basic_policy_parameters")
-  return hparams
+#@registry.register_hparams
+#def ppo_continuous_action_base():
+#  hparams = ppo_base_v1()
+#  hparams.add_hparam("policy_network", feed_forward_gaussian_fun)
+#  hparams.add_hparam("policy_network_params", "basic_policy_parameters")
+#  return hparams
 
 
 @registry.register_hparams
@@ -77,14 +78,14 @@ def basic_policy_parameters():
 @registry.register_hparams
 def ppo_discrete_action_base():
   hparams = ppo_base_v1()
-  hparams.add_hparam("policy_network", feed_forward_categorical_fun)
+  hparams.add_hparam("policy_network", "feed_forward_categorical_policy")
   return hparams
 
 
 @registry.register_hparams
 def discrete_random_action_base():
   hparams = common_hparams.basic_params1()
-  hparams.add_hparam("policy_network", random_policy_fun)
+  hparams.add_hparam("policy_network", "random_policy")
   return hparams
 
 
@@ -100,7 +101,7 @@ def ppo_atari_base():
   hparams.value_loss_coef = 1
   hparams.optimization_epochs = 3
   hparams.epochs_num = 1000
-  hparams.policy_network = feed_forward_cnn_small_categorical_fun
+  hparams.policy_network = "feed_forward_cnn_small_categorical_policy"
   hparams.clipping_coef = 0.2
   hparams.optimization_batch_size = 20
   hparams.max_gradients_norm = 0.5
@@ -157,23 +158,36 @@ def get_policy(observations, hparams, action_space):
   """Get a policy network.
 
   Args:
-    observations: Tensor with observations
+    observations
     hparams: parameters
     action_space: action space
 
   Returns:
-    Tensor with policy and value function output
+    Tuple (action logits, value).
   """
-  policy_network_lambda = hparams.policy_network
-  return policy_network_lambda(action_space, hparams, observations)
+  if not isinstance(action_space, gym.spaces.Discrete):
+    raise ValueError("Expecting discrete action space.")
+
+  model = registry.model(hparams.policy_network)(
+      hparams, tf.estimator.ModeKeys.TRAIN
+  )
+  obs_shape = common_layers.shape_list(observations)
+  features = {
+      "inputs": observations,
+      "target_action": tf.zeros(obs_shape[:2] + [action_space.n]),
+      "target_value": tf.zeros(obs_shape[:2])
+  }
+  with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
+    (targets, _) = model(features)
+  return (targets["target_action"], targets["target_value"])
 
 
 @registry.register_hparams
 def ppo_pong_ae_base():
   """Pong autoencoder base parameters."""
   hparams = ppo_original_params()
   hparams.learning_rate = 1e-4
-  hparams.network = dense_bitwise_categorical_fun
+  hparams.network = "dense_bitwise_categorical_policy"
   return hparams
 
 
@@ -225,6 +239,12 @@ def mfrl_original():
       batch_size=16,
       eval_batch_size=2,
       frame_stack_size=4,
+      eval_sampling_temps=[0.0, 0.2, 0.5, 0.8, 1.0, 2.0],
+      eval_max_num_noops=8,
+      resize_height_factor=2,
+      resize_width_factor=2,
+      grayscale=0,
+      env_timesteps_limit=-1,
   )
 
 
@@ -234,11 +254,6 @@ def mfrl_base():
   hparams = mfrl_original()
   hparams.add_hparam("ppo_epochs_num", 3000)
   hparams.add_hparam("ppo_eval_every_epochs", 100)
-  hparams.add_hparam("eval_max_num_noops", 8)
-  hparams.add_hparam("resize_height_factor", 2)
-  hparams.add_hparam("resize_width_factor", 2)
-  hparams.add_hparam("grayscale", 0)
-  hparams.add_hparam("env_timesteps_limit", -1)
   return hparams
 
 
@@ -250,10 +265,18 @@ def mfrl_tiny():
   return hparams
 
 
+class DiscretePolicyBase(t2t_model.T2TModel):
+
+  @staticmethod
+  def _get_num_actions(features):
+    return common_layers.shape_list(features["target_action"])[2]
+
+
 NetworkOutput = collections.namedtuple(
     "NetworkOutput", "policy, value, action_postprocessing")
 
 
+# TODO(koz4k): Translate it to T2TModel or remove.
 def feed_forward_gaussian_fun(action_space, config, observations):
   """Feed-forward Gaussian."""
   if not isinstance(action_space, gym.spaces.box.Box):
@@ -303,36 +326,40 @@ def clip_logits(logits, config):
     return logits
 
 
-def feed_forward_categorical_fun(action_space, config, observations):
+@registry.register_model
+class FeedForwardCategoricalPolicy(DiscretePolicyBase):
   """Feed-forward categorical."""
-  if not isinstance(action_space, gym.spaces.Discrete):
-    raise ValueError("Expecting discrete action space.")
-  flat_observations = tf.reshape(observations, [
-      tf.shape(observations)[0], tf.shape(observations)[1],
-      functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
-  with tf.variable_scope("network_parameters"):
+
+  def body(self, features):
+    observations = features["inputs"]
+    flat_observations = tf.reshape(observations, [
+        tf.shape(observations)[0], tf.shape(observations)[1],
+        functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
     with tf.variable_scope("policy"):
       x = flat_observations
-      for size in config.policy_layers:
+      for size in self.hparams.policy_layers:
         x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
-      logits = tf.contrib.layers.fully_connected(x, action_space.n,
-                                                 activation_fn=None)
+      logits = tf.contrib.layers.fully_connected(
+          x, self._get_num_actions(features), activation_fn=None
+      )
     with tf.variable_scope("value"):
       x = flat_observations
-      for size in config.value_layers:
+      for size in self.hparams.value_layers:
         x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
       value = tf.contrib.layers.fully_connected(x, 1, None)[..., 0]
-  logits = clip_logits(logits, config)
-  policy = tfp.distributions.Categorical(logits=logits)
-  return NetworkOutput(policy, value, lambda a: a)
+    logits = clip_logits(logits, self.hparams)
+    return {"target_action": logits, "target_value": value}
 
 
-def feed_forward_cnn_small_categorical_fun(action_space, config, observations):
+@registry.register_model
+class FeedForwardCnnSmallCategoricalPolicy(DiscretePolicyBase):
   """Small cnn network with categorical output."""
-  obs_shape = common_layers.shape_list(observations)
-  x = tf.reshape(observations, [-1] + obs_shape[2:])
-  with tf.variable_scope("network_parameters"):
-    dropout = getattr(config, "dropout_ppo", 0.0)
+
+  def body(self, features):
+    observations = features["inputs"]
+    obs_shape = common_layers.shape_list(observations)
+    x = tf.reshape(observations, [-1] + obs_shape[2:])
+    dropout = getattr(self.hparams, "dropout_ppo", 0.0)
     with tf.variable_scope("feed_forward_cnn_small"):
       x = tf.to_float(x) / 255.0
       x = tf.contrib.layers.conv2d(x, 32, [5, 5], [2, 2],
@@ -346,23 +373,25 @@ def feed_forward_cnn_small_categorical_fun(action_space, config, observations):
       flat_x = tf.nn.dropout(flat_x, keep_prob=1.0 - dropout)
       x = tf.contrib.layers.fully_connected(flat_x, 128, tf.nn.relu)
 
-      logits = tf.contrib.layers.fully_connected(x, action_space.n,
-                                                 activation_fn=None)
-      logits = clip_logits(logits, config)
+      logits = tf.contrib.layers.fully_connected(
+          x, self._get_num_actions(features), activation_fn=None
+      )
+      logits = clip_logits(logits, self.hparams)
 
       value = tf.contrib.layers.fully_connected(
           x, 1, activation_fn=None)[..., 0]
-      policy = tfp.distributions.Categorical(logits=logits)
-  return NetworkOutput(policy, value, lambda a: a)
+    return {"target_action": logits, "target_value": value}
 
 
-def feed_forward_cnn_small_categorical_fun_new(
-    action_space, config, observations):
+@registry.register_model
+class FeedForwardCnnSmallCategoricalPolicyNew(DiscretePolicyBase):
   """Small cnn network with categorical output."""
-  obs_shape = common_layers.shape_list(observations)
-  x = tf.reshape(observations, [-1] + obs_shape[2:])
-  with tf.variable_scope("network_parameters"):
-    dropout = getattr(config, "dropout_ppo", 0.0)
+
+  def body(self, features):
+    observations = features["inputs"]
+    obs_shape = common_layers.shape_list(observations)
+    x = tf.reshape(observations, [-1] + obs_shape[2:])
+    dropout = getattr(self.hparams, "dropout_ppo", 0.0)
     with tf.variable_scope("feed_forward_cnn_small"):
       x = tf.to_float(x) / 255.0
       x = tf.nn.dropout(x, keep_prob=1.0 - dropout)
@@ -384,22 +413,23 @@ def feed_forward_cnn_small_categorical_fun_new(
       flat_x = tf.nn.dropout(flat_x, keep_prob=1.0 - dropout)
       x = tf.layers.dense(flat_x, 128, activation=tf.nn.relu, name="dense1")
 
-      logits = tf.layers.dense(x, action_space.n, name="dense2")
-      logits = clip_logits(logits, config)
+      logits = tf.layers.dense(
+          x, self._get_num_actions(features), name="dense2"
+      )
+      logits = clip_logits(logits, self.hparams)
 
       value = tf.layers.dense(x, 1, name="value")[..., 0]
-      policy = tfp.distributions.Categorical(logits=logits)
+    return {"target_action": logits, "target_value": value}
 
-  return NetworkOutput(policy, value, lambda a: a)
 
-
-def dense_bitwise_categorical_fun(action_space, config, observations):
+@registry.register_model
+class DenseBitwiseCategoricalPolicy(DiscretePolicyBase):
   """Dense network with bitwise input and categorical output."""
-  del config
-  obs_shape = common_layers.shape_list(observations)
-  x = tf.reshape(observations, [-1] + obs_shape[2:])
 
-  with tf.variable_scope("network_parameters"):
+  def body(self, features):
+    observations = features["inputs"]
+    obs_shape = common_layers.shape_list(observations)
+    x = tf.reshape(observations, [-1] + obs_shape[2:])
     with tf.variable_scope("dense_bitwise"):
       x = discretization.int_to_bit_embed(x, 8, 32)
       flat_x = tf.reshape(
@@ -409,22 +439,29 @@ def dense_bitwise_categorical_fun(action_space, config, observations):
       x = tf.contrib.layers.fully_connected(flat_x, 256, tf.nn.relu)
       x = tf.contrib.layers.fully_connected(flat_x, 128, tf.nn.relu)
 
-      logits = tf.contrib.layers.fully_connected(x, action_space.n,
-                                                 activation_fn=None)
+      logits = tf.contrib.layers.fully_connected(
+          x, self._get_num_actions(features), activation_fn=None
+      )
 
       value = tf.contrib.layers.fully_connected(
           x, 1, activation_fn=None)[..., 0]
-      policy = tfp.distributions.Categorical(logits=logits)
 
-  return NetworkOutput(policy, value, lambda a: a)
+    return {"target_action": logits, "target_value": value}
 
 
-def random_policy_fun(action_space, unused_config, observations):
+@registry.register_model
+class RandomPolicy(DiscretePolicyBase):
   """Random policy with categorical output."""
-  obs_shape = observations.shape.as_list()
-  with tf.variable_scope("network_parameters"):
+
+  def body(self, features):
+    observations = features["inputs"]
+    obs_shape = observations.shape.as_list()
+    # Just so Saver doesn't complain because of no variables.
+    tf.get_variable("dummy_var", initializer=0.0)
+    num_actions = self._get_num_actions(features)
+    logits = tf.constant(
+        1. / float(num_actions),
+        shape=(obs_shape[:2] + [num_actions])
+    )
     value = tf.zeros(obs_shape[:2])
-    policy = tfp.distributions.Categorical(
-        probs=[[[1. / float(action_space.n)] * action_space.n] *
-               (obs_shape[0] * obs_shape[1])])
-  return NetworkOutput(policy, value, lambda a: a)
+    return {"target_action": logits, "target_value": value}

tensor2tensor/rl/dopamine_connector.py

@@ -19,6 +19,7 @@
 from __future__ import division
 from __future__ import print_function
 
+from copy import copy
 from dopamine.agents.dqn import dqn_agent
 from dopamine.agents.dqn.dqn_agent import NATURE_DQN_OBSERVATION_SHAPE
 from dopamine.agents.dqn.dqn_agent import NATURE_DQN_STACK_SIZE
@@ -285,6 +286,7 @@ def train(self,
             simulated,
             save_continuously,
             epoch,
+            sampling_temp=1.0,
             num_env_steps=None,
             env_step_multiplier=1,
             eval_env_fn=None,
@@ -294,6 +296,11 @@ def train(self,
     if num_env_steps is None:
       num_env_steps = hparams.num_frames
 
+    hparams = copy(hparams)
+    hparams.set_hparam(
+        "agent_epsilon_eval", min(hparams.agent_epsilon_eval * sampling_temp, 1)
+    )
+
     target_iterations, training_steps_per_iteration = \
       self._target_iteractions_and_steps(
           num_env_steps=num_env_steps * env_step_multiplier,
@@ -307,11 +314,14 @@ def train(self,
 
     self.completed_iterations = target_iterations
 
-  def evaluate(self, env_fn, hparams, stochastic):
+  def evaluate(self, env_fn, hparams, sampling_temp):
     target_iterations = 0
     training_steps_per_iteration = 0
-    if not stochastic:
-      hparams.set_hparam("agent_epsilon_eval", 0.)
+
+    hparams = copy(hparams)
+    hparams.set_hparam(
+        "agent_epsilon_eval", min(hparams.agent_epsilon_eval * sampling_temp, 1)
+    )
 
     create_environment_fn = get_create_env_fun(
         env_fn, time_limit=hparams.time_limit)

tensor2tensor/rl/policy_learner.py

@@ -29,13 +29,21 @@ def __init__(self, frame_stack_size, base_event_dir, agent_model_dir):
     self.agent_model_dir = agent_model_dir
 
   def train(
-      self, env_fn, hparams, simulated, save_continuously, epoch,
-      num_env_steps=None, env_step_multiplier=1, eval_env_fn=None,
+      self,
+      env_fn,
+      hparams,
+      simulated,
+      save_continuously,
+      epoch,
+      sampling_temp=1.0,
+      num_env_steps=None,
+      env_step_multiplier=1,
+      eval_env_fn=None,
       report_fn=None
   ):
     # TODO(konradczechowski): pass name_scope instead of epoch?
     # TODO(konradczechowski): move 'simulated' to  batch_env
     raise NotImplementedError()
 
-  def evaluate(self, env_fn, hparams, stochastic):
+  def evaluate(self, env_fn, hparams, sampling_temp):
     raise NotImplementedError()