DDPG

task.py

@@ -27,6 +27,8 @@ class BasicTask:
             next_state = self.normalize_state(next_state)
         return next_state, np.sign(reward), done, info
 
+
+
 class MountainCar(BasicTask):
     name = 'MountainCar-v0'
     success_threshold = -110
@@ -71,3 +73,29 @@ class PixelAtari(BasicTask):
 
     def normalize_state(self, state):
         return np.asarray(state, dtype=np.float32) / 255.0
+
+
+class ContinuousMountainCar(BasicTask):
+    name = 'MountainCarContinuous-v0'
+    success_threshold = 1000
+
+    def __init__(self):
+        BasicTask.__init__(self)
+        self.env = gym.make(self.name)
+        self.env._max_episode_steps = sys.maxsize
+
+
+class Pendulum(BasicTask):
+    name = 'Pendulum-v0'
+    success_threshold = 200
+
+    def __init__(self):
+        BasicTask.__init__(self)
+        self.env = gym.make(self.name)
+        self.env._max_episode_steps = sys.maxsize
+
+    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, reward, done, info