fix relative import

baselines/gail/README.md

@@ -41,4 +41,4 @@ Thanks to the open source:
 - @openai/imitation
 - @carpedm20/deep-rl-tensorflow
 
-Also, thanks [Ryan Jilian](https://github.com/ryanjulian) for reviewing the code
+Also, thanks [Ryan Julian](https://github.com/ryanjulian) for reviewing the code

baselines/gail/behavior_clone.py

@@ -11,14 +11,14 @@ from tqdm import tqdm
 
 import tensorflow as tf
 
-import mlp_policy
+from baselines.gail import mlp_policy
 from baselines import bench
 from baselines import logger
 from baselines.common import set_global_seeds, tf_util as U
 from baselines.common.misc_util import boolean_flag
 from baselines.common.mpi_adam import MpiAdam
-from run_mujoco import runner
-from dataset.mujoco_dset import Mujoco_Dset
+from baselines.gail.run_mujoco import runner
+from baselines.gail.dataset.mujoco_dset import Mujoco_Dset
 
 
 def argsparser():

baselines/gail/gail-eval.py

@@ -12,11 +12,11 @@ import matplotlib.pyplot as plt
 import numpy as np
 import tensorflow as tf
 
-import run_mujoco
-import mlp_policy
+from baselines.gail import run_mujoco
+from baselines.gail import mlp_policy
 from baselines.common import set_global_seeds, tf_util as U
 from baselines.common.misc_util import boolean_flag
-from dataset.mujoco_dset import Mujoco_Dset
+from baselines.gail.dataset.mujoco_dset import Mujoco_Dset
 
 
 plt.style.use('ggplot')

baselines/gail/run_mujoco.py

@@ -11,13 +11,13 @@ from tqdm import tqdm
 import numpy as np
 import gym
 
-import mlp_policy
+from baselines.gail import mlp_policy
 from baselines.common import set_global_seeds, tf_util as U
 from baselines.common.misc_util import boolean_flag
 from baselines import bench
 from baselines import logger
-from dataset.mujoco_dset import Mujoco_Dset
-from adversary import TransitionClassifier
+from baselines.gail.dataset.mujoco_dset import Mujoco_Dset
+from baselines.gail.adversary import TransitionClassifier
 
 
 def argsparser():
@@ -125,12 +125,12 @@ def train(env, seed, policy_fn, reward_giver, dataset, algo,
     pretrained_weight = None
     if pretrained and (BC_max_iter > 0):
         # Pretrain with behavior cloning
-        import behavior_clone
+        from baselines.gail import behavior_clone
         pretrained_weight = behavior_clone.learn(env, policy_fn, dataset,
                                                  max_iters=BC_max_iter)
 
     if algo == 'trpo':
-        import trpo_mpi
+        from baselines.gail import trpo_mpi
         # Set up for MPI seed
         rank = MPI.COMM_WORLD.Get_rank()
         if rank != 0:

baselines/gail/trpo_mpi.py

@@ -17,7 +17,7 @@ from baselines import logger
 from baselines.common import colorize
 from baselines.common.mpi_adam import MpiAdam
 from baselines.common.cg import cg
-from statistics import stats
+from baselines.gail.statistics import stats
 
 
 def traj_segment_generator(pi, env, reward_giver, horizon, stochastic):