DDPG Pendulum

component/task.py

@@ -80,22 +80,6 @@ class Pendulum(BasicTask):
     name = 'Pendulum-v0'
     success_threshold = -10
 
-    def __init__(self):
-        BasicTask.__init__(self)
-        self.env = gym.make(self.name)
-        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
-
-class MountainCarContinuous(BasicTask):
-    name = 'MountainCarContinuous-v0'
-    success_threshold = 90
-
     def __init__(self):
         BasicTask.__init__(self)
         self.env = gym.make(self.name)
@@ -104,15 +88,15 @@ class MountainCarContinuous(BasicTask):
         self.state_dim = self.env.observation_space.shape[0]
 
     def normalize_state(self, state):
-        state = (state - self.env.unwrapped.low_state) / \
-                (self.env.unwrapped.high_state - self.env.unwrapped.low_state)
+        state = (state - self.env.observation_space.low) / \
+                (self.env.observation_space.high - self.env.observation_space.low)
         state = state * 2 - 1
         return state
 
     def step(self, action):
-        action = np.clip(action, -1, 1)
+        action = np.clip(action, -2, 2)
         next_state, reward, done, info = self.env.step(action)
-        return next_state, reward, done, info
+        return self.normalize_state(next_state), reward, done, info
 
 class BipedalWalker(BasicTask):
     name = 'BipedalWalker-v2'