Model-Based RL: Player

tensor2tensor/data_generators/gym_env.py

@@ -21,7 +21,10 @@
 
 import collections
 import itertools
+import os
 import random
+import re
+
 from gym.spaces import Box
 import numpy as np
 
@@ -185,7 +188,10 @@ def current_epoch_rollouts(self, split=None, minimal_rollout_frames=0):
     if not rollouts_by_split:
       if split is not None:
         raise ValueError(
-            "generate_data() should first be called in the current epoch"
+            "Data is not splitted into train/dev/test. If data created by "
+            "environment interaction (NOT loaded from disk) you should call "
+            "generate_data() first. Note that generate_data() will write to "
+            "disk and can corrupt your experiment data."
         )
       else:
         rollouts = self._current_epoch_rollouts
@@ -636,6 +642,53 @@ def base_env_name(self):
   def num_channels(self):
     return self.observation_space.shape[2]
 
+  @staticmethod
+  def infer_last_epoch_num(data_dir):
+    """Infer highest epoch number from file names in data_dir."""
+    names = os.listdir(data_dir)
+    epochs_str = [re.findall(pattern=r".*\.(-?\d+)$", string=name)
+                  for name in names]
+    epochs_str = sum(epochs_str, [])
+    return max([int(epoch_str) for epoch_str in epochs_str])
+
+  @staticmethod
+  def setup_env_from_hparams(hparams, batch_size, max_num_noops):
+    game_mode = "NoFrameskip-v4"
+    camel_game_name = misc_utils.snakecase_to_camelcase(hparams.game)
+    camel_game_name += game_mode
+    env_name = camel_game_name
+
+    env = T2TGymEnv(base_env_name=env_name,
+                    batch_size=batch_size,
+                    grayscale=hparams.grayscale,
+                    resize_width_factor=hparams.resize_width_factor,
+                    resize_height_factor=hparams.resize_height_factor,
+                    rl_env_max_episode_steps=hparams.rl_env_max_episode_steps,
+                    max_num_noops=max_num_noops, maxskip_envs=True)
+    return env
+
+  @staticmethod
+  def setup_and_load_epoch(hparams, data_dir, which_epoch_data=None):
+    """Load T2TBatchGymEnv with data from one epoch.
+
+    Args:
+        which_epoch_data: data from which epoch to load.
+    """
+    t2t_env = T2TGymEnv.setup_env_from_hparams(
+        hparams, batch_size=hparams.real_batch_size,
+        max_num_noops=hparams.max_num_noops
+    )
+    # Load data.
+    if which_epoch_data is not None:
+      if which_epoch_data == "last":
+        which_epoch_data = T2TGymEnv.infer_last_epoch_num(data_dir)
+      assert isinstance(which_epoch_data, int), \
+        "{}".format(type(which_epoch_data))
+      t2t_env.start_new_epoch(which_epoch_data, data_dir)
+    else:
+      t2t_env.start_new_epoch(-999)
+    return t2t_env
+
   def _derive_observation_space(self, orig_observ_space):
     height, width, channels = orig_observ_space.shape
     if self.grayscale:

tensor2tensor/models/research/rl.py

@@ -203,6 +203,28 @@ def env_fn(in_graph):
   return env_fn
 
 
+def make_simulated_env_fn_from_hparams(
+    real_env, hparams, batch_size, initial_frame_chooser, model_dir,
+    sim_video_dir=None):
+  """Creates a simulated env_fn."""
+  model_hparams = trainer_lib.create_hparams(hparams.generative_model_params)
+  if hparams.wm_policy_param_sharing:
+    model_hparams.optimizer_zero_grads = True
+  return make_simulated_env_fn(
+      reward_range=real_env.reward_range,
+      observation_space=real_env.observation_space,
+      action_space=real_env.action_space,
+      frame_stack_size=hparams.frame_stack_size,
+      frame_height=real_env.frame_height, frame_width=real_env.frame_width,
+      initial_frame_chooser=initial_frame_chooser, batch_size=batch_size,
+      model_name=hparams.generative_model,
+      model_hparams=trainer_lib.create_hparams(hparams.generative_model_params),
+      model_dir=model_dir,
+      intrinsic_reward_scale=hparams.intrinsic_reward_scale,
+      sim_video_dir=sim_video_dir,
+  )
+
+
 def get_policy(observations, hparams, action_space):
   """Get a policy network.
 

tensor2tensor/rl/envs/simulated_batch_env.py

@@ -161,9 +161,12 @@ def initialize(self, sess):
     model_loader = tf.train.Saver(
         var_list=tf.global_variables(scope="next_frame*")  # pylint:disable=unexpected-keyword-arg
     )
-    trainer_lib.restore_checkpoint(
-        self._model_dir, saver=model_loader, sess=sess, must_restore=True
-    )
+    if os.path.isdir(self._model_dir):
+      trainer_lib.restore_checkpoint(
+          self._model_dir, saver=model_loader, sess=sess, must_restore=True
+      )
+    else:
+      model_loader.restore(sess=sess, save_path=self._model_dir)
 
   def __str__(self):
     return "SimulatedEnv"

tensor2tensor/rl/envs/simulated_batch_gym_env.py

@@ -26,8 +26,7 @@
 
 
 class FlatBatchEnv(Env):
-  """TODO(konradczechowski): Add doc-string."""
-
+  """Gym environment interface for Batched Environments (with batch size = 1)"""
   def __init__(self, batch_env):
     if batch_env.batch_size != 1:
       raise ValueError("Number of environments in batch must be equal to one")