thu-ml · Trinkle23897 · Mar 11, 2021 · Mar 9, 2021 · Mar 9, 2021 · Mar 11, 2021
diff --git a/test/base/test_utils.py b/test/base/test_utils.py
@@ -1,8 +1,8 @@
 import torch
 import numpy as np
 
-from tianshou.utils import MovAvg
 from tianshou.utils.net.common import MLP, Net
+from tianshou.utils import MovAvg, RunningMeanStd
 from tianshou.exploration import GaussianNoise, OUNoise
 from tianshou.utils.net.continuous import RecurrentActorProb, RecurrentCritic
 
@@ -30,6 +30,16 @@ def test_moving_average():
     assert np.allclose(stat.std() ** 2, 2)
 
 
+def test_rms():
+    rms = RunningMeanStd()
+    assert np.allclose(rms.mean, 0)
+    assert np.allclose(rms.var, 1)
+    rms.update(np.array([[[1, 2], [3, 5]]]))
+    rms.update(np.array([[[1, 2], [3, 4]], [[1, 2], [0, 0]]]))
+    assert np.allclose(rms.mean, np.array([[1, 2], [2, 3]]), atol=1e-3)
+    assert np.allclose(rms.var, np.array([[0, 0], [2, 14 / 3.]]), atol=1e-3)
+
+
 def test_net():
     # here test the networks that does not appear in the other script
     bsz = 64
@@ -79,4 +89,5 @@ def test_net():
 if __name__ == '__main__':
     test_noise()
     test_moving_average()
+    test_rms()
     test_net()
diff --git a/tianshou/env/venvs.py b/tianshou/env/venvs.py
@@ -2,6 +2,7 @@
 import numpy as np
 from typing import Any, List, Union, Optional, Callable
 
+from tianshou.utils import RunningMeanStd
 from tianshou.env.worker import EnvWorker, DummyEnvWorker, SubprocEnvWorker, \
     RayEnvWorker
 
@@ -55,6 +56,13 @@ def seed(self, seed):
     :param float timeout: use in asynchronous simulation same as above, in each
         vectorized step it only deal with those environments spending time
         within ``timeout`` seconds.
+    :param bool norm_obs: Whether to track mean/std of data and normalise observation
+        on return. For now, observation normalization only support observation of
+        type np.ndarray.
+    :param obs_rms: class to track mean&std of observation. If not given, it will
+        initialize a new one. Usually in envs that is used to evaluate algorithm,
+        obs_rms should be passed in. Default to None.
+    :param bool update_obs_rms: Whether to update obs_rms. Default to True.
     """
 
     def __init__(
@@ -63,6 +71,9 @@ def __init__(
         worker_fn: Callable[[Callable[[], gym.Env]], EnvWorker],
         wait_num: Optional[int] = None,
         timeout: Optional[float] = None,
+        norm_obs: bool = False,
+        obs_rms: Optional[RunningMeanStd] = None,
+        update_obs_rms: bool = True,
     ) -> None:
         self._env_fns = env_fns
         # A VectorEnv contains a pool of EnvWorkers, which corresponds to
@@ -90,6 +101,12 @@ def __init__(
         self.ready_id = list(range(self.env_num))
         self.is_closed = False
 
+        # initialize observation running mean/std
+        self.norm_obs = norm_obs
+        self.update_obs_rms = update_obs_rms
+        self.obs_rms = RunningMeanStd() if obs_rms is None and norm_obs else obs_rms
+        self.__eps = np.finfo(np.float32).eps.item()
+
     def _assert_is_not_closed(self) -> None:
         assert not self.is_closed, \
             f"Methods of {self.__class__.__name__} cannot be called after close."
@@ -149,7 +166,9 @@ def reset(
         if self.is_async:
             self._assert_id(id)
         obs = np.stack([self.workers[i].reset() for i in id])
-        return obs
+        if self.obs_rms and self.update_obs_rms:
+            self.obs_rms.update(obs)
+        return self.normalize_obs(obs)
 
     def step(
         self,
@@ -219,7 +238,10 @@ def step(
                 info["env_id"] = env_id
                 result.append((obs, rew, done, info))
                 self.ready_id.append(env_id)
-        return list(map(np.stack, zip(*result)))
+        obs_stack, rew_stack, done_stack, info_stack = map(np.stack, zip(*result))
+        if self.obs_rms and self.update_obs_rms:
+            self.obs_rms.update(obs_stack)
+        return [self.normalize_obs(obs_stack), rew_stack, done_stack, info_stack]
 
     def seed(
         self, seed: Optional[Union[int, List[int]]] = None
@@ -255,15 +277,23 @@ def render(self, **kwargs: Any) -> List[Any]:
     def close(self) -> None:
         """Close all of the environments.
 
-        This function will be called only once (if not, it will be called
-        during garbage collected). This way, ``close`` of all workers can be
-        assured.
+        This function will be called only once (if not, it will be called during
+        garbage collected). This way, ``close`` of all workers can be assured.
         """
         self._assert_is_not_closed()
         for w in self.workers:
             w.close()
         self.is_closed = True
 
+    def normalize_obs(self, obs: np.ndarray) -> np.ndarray:
+        """Normalize observations by statistics in obs_rms."""
+        clip_max = 10.0  # this magic number is from openai baselines
+        # see baselines/common/vec_env/vec_normalize.py#L10
+        if self.obs_rms and self.norm_obs:
+            obs = (obs - self.obs_rms.mean) / np.sqrt(self.obs_rms.var + self.__eps)
+            obs = np.clip(obs, -clip_max, clip_max)
+        return obs
+
     def __del__(self) -> None:
         """Redirect to self.close()."""
         if not self.is_closed:
@@ -275,38 +305,26 @@ class DummyVectorEnv(BaseVectorEnv):
 
     .. seealso::
 
-        Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
-        explanation.
+        Please refer to :class:`~tianshou.env.BaseVectorEnv` for other APIs' usage.
     """
 
-    def __init__(
-        self,
-        env_fns: List[Callable[[], gym.Env]],
-        wait_num: Optional[int] = None,
-        timeout: Optional[float] = None,
-    ) -> None:
-        super().__init__(env_fns, DummyEnvWorker, wait_num=wait_num, timeout=timeout)
+    def __init__(self, env_fns: List[Callable[[], gym.Env]], **kwargs: Any) -> None:
+        super().__init__(env_fns, DummyEnvWorker, **kwargs)
 
 
 class SubprocVectorEnv(BaseVectorEnv):
     """Vectorized environment wrapper based on subprocess.
 
     .. seealso::
 
-        Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
-        explanation.
+        Please refer to :class:`~tianshou.env.BaseVectorEnv` for other APIs' usage.
     """
 
-    def __init__(
-        self,
-        env_fns: List[Callable[[], gym.Env]],
-        wait_num: Optional[int] = None,
-        timeout: Optional[float] = None,
-    ) -> None:
+    def __init__(self, env_fns: List[Callable[[], gym.Env]], **kwargs: Any) -> None:
         def worker_fn(fn: Callable[[], gym.Env]) -> SubprocEnvWorker:
             return SubprocEnvWorker(fn, share_memory=False)
 
-        super().__init__(env_fns, worker_fn, wait_num=wait_num, timeout=timeout)
+        super().__init__(env_fns, worker_fn, **kwargs)
 
 
 class ShmemVectorEnv(BaseVectorEnv):
@@ -316,20 +334,14 @@ class ShmemVectorEnv(BaseVectorEnv):
 
     .. seealso::
 
-        Please refer to :class:`~tianshou.env.SubprocVectorEnv` for more
-        detailed explanation.
+        Please refer to :class:`~tianshou.env.BaseVectorEnv` for other APIs' usage.
     """
 
-    def __init__(
-        self,
-        env_fns: List[Callable[[], gym.Env]],
-        wait_num: Optional[int] = None,
-        timeout: Optional[float] = None,
-    ) -> None:
+    def __init__(self, env_fns: List[Callable[[], gym.Env]], **kwargs: Any) -> None:
         def worker_fn(fn: Callable[[], gym.Env]) -> SubprocEnvWorker:
             return SubprocEnvWorker(fn, share_memory=True)
 
-        super().__init__(env_fns, worker_fn, wait_num=wait_num, timeout=timeout)
+        super().__init__(env_fns, worker_fn, **kwargs)
 
 
 class RayVectorEnv(BaseVectorEnv):
@@ -339,16 +351,10 @@ class RayVectorEnv(BaseVectorEnv):
 
     .. seealso::
 
-        Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
-        explanation.
+        Please refer to :class:`~tianshou.env.BaseVectorEnv` for other APIs' usage.
     """
 
-    def __init__(
-        self,
-        env_fns: List[Callable[[], gym.Env]],
-        wait_num: Optional[int] = None,
-        timeout: Optional[float] = None,
-    ) -> None:
+    def __init__(self, env_fns: List[Callable[[], gym.Env]], **kwargs: Any) -> None:
         try:
             import ray
         except ImportError as e:
@@ -357,4 +363,4 @@ def __init__(
             ) from e
         if not ray.is_initialized():
             ray.init()
-        super().__init__(env_fns, RayEnvWorker, wait_num=wait_num, timeout=timeout)
+        super().__init__(env_fns, RayEnvWorker, **kwargs)
diff --git a/tianshou/utils/__init__.py b/tianshou/utils/__init__.py
@@ -1,9 +1,10 @@
 from tianshou.utils.config import tqdm_config
-from tianshou.utils.moving_average import MovAvg
+from tianshou.utils.statistics import MovAvg, RunningMeanStd
 from tianshou.utils.log_tools import BasicLogger, LazyLogger, BaseLogger
 
 __all__ = [
     "MovAvg",
+    "RunningMeanStd",
     "tqdm_config",
     "BaseLogger",
     "BasicLogger",

diff --git a/tianshou/utils/moving_average.py → tianshou/utils/statistics.py b/tianshou/utils/moving_average.py → tianshou/utils/statistics.py
@@ -66,3 +66,31 @@ def std(self) -> np.number:
         if len(self.cache) == 0:
             return 0
         return np.std(self.cache)
+
+
+class RunningMeanStd(object):
+    """Calulates the running mean and std of a data stream.
+
+    https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
+    """
+
+    def __init__(self) -> None:
+        self.mean, self.var = 0.0, 1.0
+        self.count = 0
+
+    def update(self, x: np.ndarray) -> None:
+        """Add a batch of item into RMS with the same shape, modify mean/var/count."""
+        batch_mean, batch_var = np.mean(x, axis=0), np.var(x, axis=0)
+        batch_count = len(x)
+
+        delta = batch_mean - self.mean
+        total_count = self.count + batch_count
+
+        new_mean = self.mean + delta * batch_count / total_count
+        m_a = self.var * self.count
+        m_b = batch_var * batch_count
+        m_2 = m_a + m_b + delta ** 2 * self.count * batch_count / total_count
+        new_var = m_2 / total_count
+
+        self.mean, self.var = new_mean, new_var
+        self.count = total_count