ddpg with a dynamics model

I'm trying a dynamics model to provide additional supervision. I'm using
this repo because it's performance tested on a competition and is in
pytorch. I'm intially testing with pendulum. Code is messy as it's a one
time experiment.

common/env_wrappers.py

@@ -1,7 +1,8 @@
 import numpy as np
 import gym
 from gym.spaces import Box
-from osim.env import RunEnv
+import sys
+# from osim.env import RunEnv
 
 from common.state_transform import StateVelCentr
 
@@ -59,12 +60,59 @@ class DdpgWrapper(gym.Wrapper):
         return observation
 
 
+# def create_env_old(args):
+#     env = RunEnv(visualize=False, max_obstacles=args.max_obstacles)
+#
+#     if hasattr(args, "baseline_wrapper") or hasattr(args, "ddpg_wrapper"):
+#         env = DdpgWrapper(env, args)
+#
+#     return env
+
+
+# class BasicTask:
+#     def __init__(self):
+#         self.normalized_state = True
+#
+#     def normalize_state(self, state):
+#         return state
+#
+#     def reset(self):
+#         state = self.env.reset()
+#         if self.normalized_state:
+#             return self.normalize_state(state)
+#         return state
+#
+#     def step(self, action):
+#         next_state, reward, done, info = self.env.step(action)
+#         if self.normalized_state:
+#             next_state = self.normalize_state(next_state)
+#         return next_state, np.sign(reward), done, info
+#
+#     def random_action(self):
+#         return self.env.action_space.sample()
+#
+#
+# class Pendulum(BasicTask):
+#     name = 'Pendulum-v0'
+#     success_threshold = -10
+#
+#     def __init__(self):
+#         BasicTask.__init__(self)
+#         self.env = gym.make(self.name)
+#         self.max_episode_steps = self.env._max_episode_steps
+#         self.env._max_episode_steps = sys.maxsize
+#         self.action_dim = self.env.action_space.shape[0]
+#         self.state_dim = self.env.observation_space.shape[0]
+#
+#     def step(self, action):
+#         action = np.clip(action, -2, 2)
+#         next_state, reward, done, info = self.env.step(action)
+#         return next_state, reward, done, info
+
+
 def create_env(args):
-    env = RunEnv(visualize=False, max_obstacles=args.max_obstacles)
-
-    if hasattr(args, "baseline_wrapper") or hasattr(args, "ddpg_wrapper"):
-        env = DdpgWrapper(env, args)
-
+    # env = Pendulum()
+    env = gym.make('Pendulum-v0')
     return env