Seeding update

docs/creating_environments.md

@@ -50,6 +50,7 @@
 
 * `gym-foo/gym_foo/envs/foo_env.py` should look something like:
   ```python
+  from typing import Optional
   import gym
   from gym import error, spaces, utils
   from gym.utils import seeding
@@ -61,7 +62,8 @@
       ...
     def step(self, action):
       ...
-    def reset(self):
+    def reset(self, seed: Optional[int] = None):
+      super().reset(seed=seed)
       ...
     def render(self, mode='human'):
       ...

gym/core.py

@@ -1,8 +1,10 @@
 from abc import abstractmethod
+from typing import Optional
 
 import gym
 from gym import error
-from gym.utils import closer
+from gym.utils import closer, seeding
+from gym.logger import warn
 
 
 class Env(object):
@@ -38,6 +40,9 @@ class Env(object):
     action_space = None
     observation_space = None
 
+    # Created
+    np_random = None
+
     @abstractmethod
     def step(self, action):
         """Run one timestep of the environment's dynamics. When end of
@@ -58,7 +63,7 @@ def step(self, action):
         raise NotImplementedError
 
     @abstractmethod
-    def reset(self):
+    def reset(self, seed: Optional[int] = None):
         """Resets the environment to an initial state and returns an initial
         observation.
 
@@ -71,7 +76,9 @@ def reset(self):
         Returns:
             observation (object): the initial observation.
         """
-        raise NotImplementedError
+        # Initialize the RNG if it's the first reset, or if the seed is manually passed
+        if seed is not None or self.np_random is None:
+            self.np_random, seed = seeding.np_random(seed)
 
     @abstractmethod
     def render(self, mode="human"):
@@ -136,7 +143,12 @@ def seed(self, seed=None):
               'seed'. Often, the main seed equals the provided 'seed', but
               this won't be true if seed=None, for example.
         """
-        return
+        warn(
+            "Function `env.seed(seed)` is marked as deprecated and will be removed in the future. "
+            "Please use `env.reset(seed=seed) instead."
+        )
+        self.np_random, seed = seeding.np_random(seed)
+        return [seed]
 
     @property
     def unwrapped(self):
@@ -173,7 +185,8 @@ class GoalEnv(Env):
     actual observations of the environment as per usual.
     """
 
-    def reset(self):
+    def reset(self, seed: Optional[int] = None):
+        super().reset(seed=seed)
         # Enforce that each GoalEnv uses a Goal-compatible observation space.
         if not isinstance(self.observation_space, gym.spaces.Dict):
             raise error.Error(
@@ -288,8 +301,8 @@ def metadata(self, value):
     def step(self, action):
         return self.env.step(action)
 
-    def reset(self, **kwargs):
-        return self.env.reset(**kwargs)
+    def reset(self, seed: Optional[int] = None, **kwargs):
+        return self.env.reset(seed=seed, **kwargs)
 
     def render(self, mode="human", **kwargs):
         return self.env.render(mode, **kwargs)
@@ -315,8 +328,8 @@ def unwrapped(self):
 
 
 class ObservationWrapper(Wrapper):
-    def reset(self, **kwargs):
-        observation = self.env.reset(**kwargs)
+    def reset(self, seed: Optional[int] = None, **kwargs):
+        observation = self.env.reset(seed=seed, **kwargs)
         return self.observation(observation)
 
     def step(self, action):
@@ -329,8 +342,8 @@ def observation(self, observation):
 
 
 class RewardWrapper(Wrapper):
-    def reset(self, **kwargs):
-        return self.env.reset(**kwargs)
+    def reset(self, seed: Optional[int] = None, **kwargs):
+        return self.env.reset(seed=seed, **kwargs)
 
     def step(self, action):
         observation, reward, done, info = self.env.step(action)
@@ -342,8 +355,8 @@ def reward(self, reward):
 
 
 class ActionWrapper(Wrapper):
-    def reset(self, **kwargs):
-        return self.env.reset(**kwargs)
+    def reset(self, seed: Optional[int] = None, **kwargs):
+        return self.env.reset(seed=seed, **kwargs)
 
     def step(self, action):
         return self.env.step(self.action(action))

gym/envs/box2d/bipedal_walker.py

@@ -1,5 +1,6 @@
 import sys
 import math
+from typing import Optional
 
 import numpy as np
 import Box2D
@@ -122,7 +123,6 @@ class BipedalWalker(gym.Env, EzPickle):
 
     def __init__(self):
         EzPickle.__init__(self)
-        self.seed()
         self.viewer = None
 
         self.world = Box2D.b2World()
@@ -149,10 +149,6 @@ def __init__(self):
         )
         self.observation_space = spaces.Box(-high, high)
 
-    def seed(self, seed=None):
-        self.np_random, seed = seeding.np_random(seed)
-        return [seed]
-
     def _destroy(self):
         if not self.terrain:
             return
@@ -188,7 +184,7 @@ def _generate_terrain(self, hardcore):
                 y += velocity
 
             elif state == PIT and oneshot:
-                counter = self.np_random.randint(3, 5)
+                counter = self.np_random.integers(3, 5)
                 poly = [
                     (x, y),
                     (x + TERRAIN_STEP, y),
@@ -215,7 +211,7 @@ def _generate_terrain(self, hardcore):
                     y -= 4 * TERRAIN_STEP
 
             elif state == STUMP and oneshot:
-                counter = self.np_random.randint(1, 3)
+                counter = self.np_random.integers(1, 3)
                 poly = [
                     (x, y),
                     (x + counter * TERRAIN_STEP, y),
@@ -228,9 +224,9 @@ def _generate_terrain(self, hardcore):
                 self.terrain.append(t)
 
             elif state == STAIRS and oneshot:
-                stair_height = +1 if self.np_random.rand() > 0.5 else -1
-                stair_width = self.np_random.randint(4, 5)
-                stair_steps = self.np_random.randint(3, 5)
+                stair_height = +1 if self.np_random.random() > 0.5 else -1
+                stair_width = self.np_random.integers(4, 5)
+                stair_steps = self.np_random.integers(3, 5)
                 original_y = y
                 for s in range(stair_steps):
                     poly = [
@@ -266,9 +262,9 @@ def _generate_terrain(self, hardcore):
             self.terrain_y.append(y)
             counter -= 1
             if counter == 0:
-                counter = self.np_random.randint(TERRAIN_GRASS / 2, TERRAIN_GRASS)
+                counter = self.np_random.integers(TERRAIN_GRASS / 2, TERRAIN_GRASS)
                 if state == GRASS and hardcore:
-                    state = self.np_random.randint(1, _STATES_)
+                    state = self.np_random.integers(1, _STATES_)
                     oneshot = True
                 else:
                     state = GRASS
@@ -312,7 +308,8 @@ def _generate_clouds(self):
             x2 = max([p[0] for p in poly])
             self.cloud_poly.append((poly, x1, x2))
 
-    def reset(self):
+    def reset(self, seed: Optional[int] = None):
+        super().reset(seed=seed)
         self._destroy()
         self.world.contactListener_bug_workaround = ContactDetector(self)
         self.world.contactListener = self.world.contactListener_bug_workaround

gym/envs/box2d/car_racing.py

@@ -32,6 +32,8 @@
 """
 import sys
 import math
+from typing import Optional
+
 import numpy as np
 
 import Box2D
@@ -121,7 +123,6 @@ class CarRacing(gym.Env, EzPickle):
 
     def __init__(self, verbose=1):
         EzPickle.__init__(self)
-        self.seed()
         self.contactListener_keepref = FrictionDetector(self)
         self.world = Box2D.b2World((0, 0), contactListener=self.contactListener_keepref)
         self.viewer = None
@@ -145,10 +146,6 @@ def __init__(self, verbose=1):
             low=0, high=255, shape=(STATE_H, STATE_W, 3), dtype=np.uint8
         )
 
-    def seed(self, seed=None):
-        self.np_random, seed = seeding.np_random(seed)
-        return [seed]
-
     def _destroy(self):
         if not self.road:
             return
@@ -343,7 +340,8 @@ def _create_track(self):
         self.track = track
         return True
 
-    def reset(self):
+    def reset(self, seed: Optional[int] = None):
+        super().reset(seed=seed)
         self._destroy()
         self.reward = 0.0
         self.prev_reward = 0.0

gym/envs/box2d/lunar_lander.py

@@ -28,6 +28,8 @@
 
 import math
 import sys
+from typing import Optional
+
 import numpy as np
 
 import Box2D
@@ -93,7 +95,6 @@ class LunarLander(gym.Env, EzPickle):
 
     def __init__(self):
         EzPickle.__init__(self)
-        self.seed()
         self.viewer = None
 
         self.world = Box2D.b2World()
@@ -117,10 +118,6 @@ def __init__(self):
             # Nop, fire left engine, main engine, right engine
             self.action_space = spaces.Discrete(4)
 
-    def seed(self, seed=None):
-        self.np_random, seed = seeding.np_random(seed)
-        return [seed]
-
     def _destroy(self):
         if not self.moon:
             return
@@ -133,7 +130,8 @@ def _destroy(self):
         self.world.DestroyBody(self.legs[0])
         self.world.DestroyBody(self.legs[1])
 
-    def reset(self):
+    def reset(self, seed: Optional[int] = None):
+        super().reset(seed=seed)
         self._destroy()
         self.world.contactListener_keepref = ContactDetector(self)
         self.world.contactListener = self.world.contactListener_keepref
@@ -505,10 +503,9 @@ def heuristic(env, s):
 
 
 def demo_heuristic_lander(env, seed=None, render=False):
-    env.seed(seed)
     total_reward = 0
     steps = 0
-    s = env.reset()
+    s = env.reset(seed=seed)
     while True:
         a = heuristic(env, s)
         s, r, done, info = env.step(a)

gym/envs/classic_control/acrobot.py

@@ -1,4 +1,6 @@
 """classic Acrobot task"""
+from typing import Optional
+
 import numpy as np
 from numpy import sin, cos, pi
 
@@ -95,13 +97,9 @@ def __init__(self):
         self.observation_space = spaces.Box(low=low, high=high, dtype=np.float32)
         self.action_space = spaces.Discrete(3)
         self.state = None
-        self.seed()
-
-    def seed(self, seed=None):
-        self.np_random, seed = seeding.np_random(seed)
-        return [seed]
 
-    def reset(self):
+    def reset(self, seed: Optional[int] = None):
+        super().reset(seed=seed)
         self.state = self.np_random.uniform(low=-0.1, high=0.1, size=(4,)).astype(
             np.float32
         )

gym/envs/classic_control/cartpole.py

@@ -5,6 +5,8 @@
 """
 
 import math
+from typing import Optional
+
 import gym
 from gym import spaces, logger
 from gym.utils import seeding
@@ -90,16 +92,11 @@ def __init__(self):
         self.action_space = spaces.Discrete(2)
         self.observation_space = spaces.Box(-high, high, dtype=np.float32)
 
-        self.seed()
         self.viewer = None
         self.state = None
 
         self.steps_beyond_done = None
 
-    def seed(self, seed=None):
-        self.np_random, seed = seeding.np_random(seed)
-        return [seed]
-
     def step(self, action):
         err_msg = "%r (%s) invalid" % (action, type(action))
         assert self.action_space.contains(action), err_msg
@@ -158,7 +155,8 @@ def step(self, action):
 
         return np.array(self.state, dtype=np.float32), reward, done, {}
 
-    def reset(self):
+    def reset(self, seed: Optional[int] = None):
+        super().reset(seed=seed)
         self.state = self.np_random.uniform(low=-0.05, high=0.05, size=(4,))
         self.steps_beyond_done = None
         return np.array(self.state, dtype=np.float32)

gym/envs/classic_control/continuous_mountain_car.py

@@ -15,6 +15,7 @@
 """
 
 import math
+from typing import Optional
 
 import numpy as np
 
@@ -84,12 +85,6 @@ def __init__(self, goal_velocity=0):
             low=self.low_state, high=self.high_state, dtype=np.float32
         )
 
-        self.seed()
-
-    def seed(self, seed=None):
-        self.np_random, seed = seeding.np_random(seed)
-        return [seed]
-
     def step(self, action):
 
         position = self.state[0]
@@ -120,7 +115,8 @@ def step(self, action):
         self.state = np.array([position, velocity], dtype=np.float32)
         return self.state, reward, done, {}
 
-    def reset(self):
+    def reset(self, seed: Optional[int] = None):
+        super().reset(seed=seed)
         self.state = np.array([self.np_random.uniform(low=-0.6, high=-0.4), 0])
         return np.array(self.state, dtype=np.float32)