add a temp copy of TF agents (until the API stops changing or configs.py are included)

examples/pybullet/gym/pybullet_envs/agents/train_ppo.py

@@ -24,15 +24,14 @@ from __future__ import division
 from __future__ import print_function
 
 import datetime
-import functools
 import os
 
 import gym
 import tensorflow as tf
 
-from agents import tools
+from . import tools
 from . import configs
-from agents.scripts import utility
+from . import utility
 
 
 def _create_environment(config):
@@ -73,7 +72,7 @@ def _define_loop(graph, logdir, train_steps, eval_steps):
       graph.force_reset)
   loop.add_phase(
       'train', graph.done, graph.score, graph.summary, train_steps,
-      report_every=None,
+      report_every=train_steps,
       log_every=train_steps // 2,
       checkpoint_every=None,
       feed={graph.is_training: True})
@@ -100,9 +99,6 @@ def train(config, env_processes):
     Evaluation scores.
   """
   tf.reset_default_graph()
-  with config.unlocked:
-    config.policy_optimizer = getattr(tf.train, config.policy_optimizer)
-    config.value_optimizer = getattr(tf.train, config.value_optimizer)
   if config.update_every % config.num_agents:
     tf.logging.warn('Number of agents should divide episodes per update.')
   with tf.device('/cpu:0'):