Hotfix for Vecnormalize (#558)

* Hotfix for Vecnormalize

* Rename `ret` to `returns`

docs/misc/changelog.rst

@@ -4,18 +4,22 @@ Changelog
 ==========
 
 
-Release 1.2.0a3 (WIP)
+Release 1.2.0 (2021-09-03)
 ---------------------------
 
+**Hotfix for VecNormalize, training/eval mode support**
+
 Breaking Changes:
 ^^^^^^^^^^^^^^^^^
 - SB3 now requires PyTorch >= 1.8.1
+- ``VecNormalize`` ``ret`` attribute was renamed to ``returns``
 
 New Features:
 ^^^^^^^^^^^^^
 
 Bug Fixes:
 ^^^^^^^^^^
+- Hotfix for ``VecNormalize`` where the observation filter was not updated at reset (thanks @vwxyzjn)
 - Fixed model predictions when using batch normalization and dropout layers by calling ``train()`` and ``eval()`` (@davidblom603)
 - Fixed model training for DQN, TD3 and SAC so that their target nets always remain in evaluation mode (@ayeright)
 - Passing ``gradient_steps=0`` to an off-policy algorithm will result in no gradient steps being taken (vs as many gradient steps as steps done in the environment

stable_baselines3/common/vec_env/vec_normalize.py

@@ -1,4 +1,5 @@
 import pickle
+import warnings
 from copy import deepcopy
 from typing import Any, Dict, Union
 
@@ -54,7 +55,7 @@ class VecNormalize(VecEnvWrapper):
         self.clip_obs = clip_obs
         self.clip_reward = clip_reward
         # Returns: discounted rewards
-        self.ret = np.zeros(self.num_envs)
+        self.returns = np.zeros(self.num_envs)
         self.gamma = gamma
         self.epsilon = epsilon
         self.training = training
@@ -73,7 +74,7 @@ class VecNormalize(VecEnvWrapper):
         del state["venv"]
         del state["class_attributes"]
         # these attributes depend on the above and so we would prefer not to pickle
-        del state["ret"]
+        del state["returns"]
         return state
 
     def __setstate__(self, state: Dict[str, Any]) -> None:
@@ -101,7 +102,7 @@ class VecNormalize(VecEnvWrapper):
 
         # Check only that the observation_space match
         utils.check_for_correct_spaces(venv, self.observation_space, venv.action_space)
-        self.ret = np.zeros(self.num_envs)
+        self.returns = np.zeros(self.num_envs)
 
     def step_wait(self) -> VecEnvStepReturn:
         """
@@ -134,13 +135,13 @@ class VecNormalize(VecEnvWrapper):
             if "terminal_observation" in infos[idx]:
                 infos[idx]["terminal_observation"] = self.normalize_obs(infos[idx]["terminal_observation"])
 
-        self.ret[dones] = 0
+        self.returns[dones] = 0
         return obs, rewards, dones, infos
 
     def _update_reward(self, reward: np.ndarray) -> None:
         """Update reward normalization statistics."""
-        self.ret = self.ret * self.gamma + reward
-        self.ret_rms.update(self.ret)
+        self.returns = self.returns * self.gamma + reward
+        self.ret_rms.update(self.returns)
 
     def _normalize_obs(self, obs: np.ndarray, obs_rms: RunningMeanStd) -> np.ndarray:
         """
@@ -220,9 +221,13 @@ class VecNormalize(VecEnvWrapper):
         """
         obs = self.venv.reset()
         self.old_obs = obs
-        self.ret = np.zeros(self.num_envs)
+        self.returns = np.zeros(self.num_envs)
         if self.training:
-            self._update_reward(self.ret)
+            if isinstance(obs, dict) and isinstance(self.obs_rms, dict):
+                for key in self.obs_rms.keys():
+                    self.obs_rms[key].update(obs[key])
+            else:
+                self.obs_rms.update(obs)
         return self.normalize_obs(obs)
 
     @staticmethod
@@ -248,3 +253,8 @@ class VecNormalize(VecEnvWrapper):
         """
         with open(save_path, "wb") as file_handler:
             pickle.dump(self, file_handler)
+
+    @property
+    def ret(self) -> np.ndarray:
+        warnings.warn("`VecNormalize` `ret` attribute is deprecated. Please use `returns` instead.", DeprecationWarning)
+        return self.returns

stable_baselines3/version.txt

@@ -1 +1 @@
-1.2.0a3
+1.2.0

tests/test_vec_normalize.py

@@ -17,6 +17,27 @@ from stable_baselines3.common.vec_env import (
 ENV_ID = "Pendulum-v0"
 
 
+class DummyRewardEnv(gym.Env):
+    metadata = {}
+
+    def __init__(self, return_reward_idx=0):
+        self.action_space = gym.spaces.Discrete(2)
+        self.observation_space = gym.spaces.Box(low=np.array([-1.0]), high=np.array([1.0]))
+        self.returned_rewards = [0, 1, 3, 4]
+        self.return_reward_idx = return_reward_idx
+        self.t = self.return_reward_idx
+
+    def step(self, action):
+        self.t += 1
+        index = (self.t + self.return_reward_idx) % len(self.returned_rewards)
+        returned_value = self.returned_rewards[index]
+        return np.array([returned_value]), returned_value, self.t == len(self.returned_rewards), {}
+
+    def reset(self):
+        self.t = 0
+        return np.array([self.returned_rewards[self.return_reward_idx]])
+
+
 class DummyDictEnv(gym.GoalEnv):
     """
     Dummy gym goal env for testing purposes
@@ -69,6 +90,15 @@ def make_dict_env():
     return Monitor(DummyDictEnv())
 
 
+def test_deprecation():
+    venv = DummyVecEnv([lambda: gym.make("CartPole-v1")])
+    venv = VecNormalize(venv)
+    with pytest.warns(None) as record:
+        assert np.allclose(venv.ret, venv.returns)
+    # Deprecation warning when using .ret
+    assert len(record) == 1
+
+
 def check_rms_equal(rmsa, rmsb):
     if isinstance(rmsa, dict):
         for key in rmsa.keys():
@@ -93,7 +123,7 @@ def check_vec_norm_equal(norma, normb):
     assert norma.norm_obs == normb.norm_obs
     assert norma.norm_reward == normb.norm_reward
 
-    assert np.all(norma.ret == normb.ret)
+    assert np.all(norma.returns == normb.returns)
     assert norma.gamma == normb.gamma
     assert norma.epsilon == normb.epsilon
     assert norma.training == normb.training
@@ -143,6 +173,24 @@ def test_runningmeanstd():
         assert np.allclose(moments_1, moments_2)
 
 
+def test_obs_rms_vec_normalize():
+    env_fns = [lambda: DummyRewardEnv(0), lambda: DummyRewardEnv(1)]
+    env = DummyVecEnv(env_fns)
+    env = VecNormalize(env)
+    env.reset()
+    assert np.allclose(env.obs_rms.mean, 0.5, atol=1e-4)
+    assert np.allclose(env.ret_rms.mean, 0.0, atol=1e-4)
+    env.step([env.action_space.sample() for _ in range(len(env_fns))])
+    assert np.allclose(env.obs_rms.mean, 1.25, atol=1e-4)
+    assert np.allclose(env.ret_rms.mean, 2, atol=1e-4)
+
+    # Check convergence to true mean
+    for _ in range(3000):
+        env.step([env.action_space.sample() for _ in range(len(env_fns))])
+    assert np.allclose(env.obs_rms.mean, 2.0, atol=1e-3)
+    assert np.allclose(env.ret_rms.mean, 5.688, atol=1e-3)
+
+
 @pytest.mark.parametrize("make_env", [make_env, make_dict_env])
 def test_vec_env(tmp_path, make_env):
     """Test VecNormalize Object"""