[Wrappers]: RescaleAction (#1491)

* Create normalize_action.py

* Update __init__.py

* Create test_normalize_action.py

* Update normalize_action.py

* Update normalize_action.py

* Rename normalize_action.py to rescale_action.py

* Update __init__.py

* Update rescale_action.py

* Update and rename test_normalize_action.py to test_rescale_action.py

* Update test_rescale_action.py

* Update rescale_action.py

* Update rescale_action.py

* Update gym/wrappers/rescale_action.py

Thanks a lot @hartikainen !

Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com>

* Update gym/wrappers/rescale_action.py

That's a very clean way ! Thanks !

Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com>

* Update gym/wrappers/rescale_action.py

Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com>

* Update rescale_action.py

* Update gym/wrappers/rescale_action.py

Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com>

* Update rescale_action.py

* Update rescale_action.py

gym/wrappers/__init__.py

@@ -3,6 +3,7 @@ from gym.wrappers.monitor import Monitor
 from gym.wrappers.time_limit import TimeLimit
 from gym.wrappers.filter_observation import FilterObservation
 from gym.wrappers.atari_preprocessing import AtariPreprocessing
+from gym.wrappers.rescale_action import RescaleAction
 from gym.wrappers.flatten_observation import FlattenObservation
 from gym.wrappers.gray_scale_observation import GrayScaleObservation
 from gym.wrappers.frame_stack import LazyFrames

gym/wrappers/rescale_action.py

@@ -0,0 +1,32 @@
+import numpy as np
+
+import gym
+from gym import spaces
+
+
+class RescaleAction(gym.ActionWrapper):
+    r"""Rescales the continuous action space of the environment to a range [a,b].
+
+    Example::
+
+        >>> RescaleAction(env, a, b).action_space == Box(a,b)
+        True
+
+    """
+    def __init__(self, env, a, b):
+        assert isinstance(env.action_space, spaces.Box), (
+            "expected Box action space, got {}".format(type(env.action_space)))
+        assert np.less_equal(a, b).all(), (a, b)
+        super(RescaleAction, self).__init__(env)
+        self.a = np.zeros(env.action_space.shape, dtype=env.action_space.dtype) + a
+        self.b = np.zeros(env.action_space.shape, dtype=env.action_space.dtype) + b
+        self.action_space = spaces.Box(low=a, high=b, shape=env.action_space.shape, dtype=env.action_space.dtype)
+
+    def action(self, action):
+        assert np.all(np.greater_equal(action, self.a)), (action, self.a)
+        assert np.all(np.less_equal(action, self.b)), (action, self.b)
+        low = self.env.action_space.low
+        high = self.env.action_space.high
+        action = low + (high - low)*((action - self.a)/(self.b - self.a))
+        action = np.clip(action, low, high)
+        return action

gym/wrappers/test_rescale_action.py

@@ -0,0 +1,32 @@
+import pytest
+
+import numpy as np
+
+import gym
+from gym.wrappers import RescaleAction
+
+
+def test_rescale_action():
+    env = gym.make('CartPole-v1')
+    with pytest.raises(AssertionError):
+        env = RescaleAction(env, -1, 1)
+    del env
+
+    env = gym.make('Pendulum-v0')
+    wrapped_env = RescaleAction(gym.make('Pendulum-v0'), -1, 1)
+
+    seed = 0
+    env.seed(seed)
+    wrapped_env.seed(seed)
+
+    obs = env.reset()
+    wrapped_obs = wrapped_env.reset()
+    assert np.allclose(obs, wrapped_obs)
+
+    obs, reward, _, _ = env.step([1.5])
+    with pytest.raises(AssertionError):
+        wrapped_env.step([1.5])
+    wrapped_obs, wrapped_reward, _, _ = wrapped_env.step([0.75])
+
+    assert np.allclose(obs, wrapped_obs)
+    assert np.allclose(reward, wrapped_reward)