RL fixes

tensor2tensor/rl/evaluator.py

@@ -252,7 +252,7 @@ def make_env(env_type, real_env, sim_env_kwargs):
 
 def make_agent(
     agent_type, env, policy_hparams, policy_dir, sampling_temp,
-    sim_env_kwargs=None, frame_stack_size=None, rollout_agent_type=None,
+    sim_env_kwargs_fn=None, frame_stack_size=None, rollout_agent_type=None,
     batch_size=None, inner_batch_size=None, env_type=None, **planner_kwargs
 ):
   """Factory function for Agents."""
@@ -270,7 +270,7 @@ def make_agent(
           batch_size, make_agent(
               rollout_agent_type, env, policy_hparams, policy_dir,
               sampling_temp, batch_size=inner_batch_size
-          ), make_env(env_type, env.env, sim_env_kwargs),
+          ), make_env(env_type, env.env, sim_env_kwargs_fn()),
           lambda env: rl_utils.BatchStackWrapper(env, frame_stack_size),
           discount_factor=policy_hparams.gae_gamma, **planner_kwargs
       ),
@@ -302,17 +302,18 @@ def make_agent_from_hparams(
     planner_hparams, model_dir, policy_dir, sampling_temp, video_writers=()
 ):
   """Creates an Agent from hparams."""
-  sim_env_kwargs = rl.make_simulated_env_kwargs(
-      base_env, loop_hparams, batch_size=planner_hparams.batch_size,
-      model_dir=model_dir
-  )
+  def sim_env_kwargs_fn():
+      return rl.make_simulated_env_kwargs(
+          base_env, loop_hparams, batch_size=planner_hparams.batch_size,
+          model_dir=model_dir
+      )
   planner_kwargs = planner_hparams.values()
   planner_kwargs.pop("batch_size")
   planner_kwargs.pop("rollout_agent_type")
   planner_kwargs.pop("env_type")
   return make_agent(
       agent_type, stacked_env, policy_hparams, policy_dir, sampling_temp,
-      sim_env_kwargs, loop_hparams.frame_stack_size,
+      sim_env_kwargs_fn, loop_hparams.frame_stack_size,
       planner_hparams.rollout_agent_type,
       inner_batch_size=planner_hparams.batch_size,
       env_type=planner_hparams.env_type,

tensor2tensor/rl/rl_utils.py

@@ -414,9 +414,8 @@ def augment_observation(
       (1, 15), "f:{:3}".format(int(frame_index)),
       fill=(255, 0, 0)
   )
-  header = np.asarray(img)
+  header = np.copy(np.asarray(img))
   del img
-  header.setflags(write=1)
   if bar_color is not None:
     header[0, :, :] = bar_color
   return np.concatenate([header, observation], axis=0)