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

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

@@ -20,21 +20,21 @@ from __future__ import print_function
 
 import functools
 
-from agents import ppo
-from agents.scripts import networks
+from . import ppo
+from . import networks
 from pybullet_envs.bullet import minitaur_gym_env
 from pybullet_envs.bullet import minitaur_duck_gym_env
 from pybullet_envs.bullet import minitaur_env_randomizer
 import pybullet_envs.bullet.minitaur_gym_env as minitaur_gym_env
 import pybullet_envs
-
+import tensorflow as tf
 
 def default():
   """Default configuration for PPO."""
   # General
   algorithm = ppo.PPOAlgorithm
-  num_agents = 10
-  eval_episodes = 20
+  num_agents = 30
+  eval_episodes = 30
   use_gpu = False
   # Network
   network = networks.feed_forward_gaussian
@@ -44,18 +44,17 @@ def default():
       value=r'.*/value/.*')
   policy_layers = 200, 100
   value_layers = 200, 100
-  init_mean_factor = 0.05
+  init_mean_factor = 0.1
   init_logstd = -1
   # Optimization
-  update_every = 20
-  policy_optimizer = 'AdamOptimizer'
-  value_optimizer = 'AdamOptimizer'
-  update_epochs_policy = 50
-  update_epochs_value = 50
-  policy_lr = 1e-4
-  value_lr = 3e-4
+  update_every = 30
+  update_epochs = 25
+  optimizer = tf.train.AdamOptimizer
+  update_epochs_policy = 64
+  update_epochs_value = 64
+  learning_rate = 1e-4  
   # Losses
-  discount = 0.985
+  discount = 0.995
   kl_target = 1e-2
   kl_cutoff_factor = 2
   kl_cutoff_coef = 1000