Merge branch 'master' into feat/set_options

docs/misc/changelog.rst

@@ -4,7 +4,7 @@ Changelog
 ==========
 
 
-Release 2.1.0a3 (WIP)
+Release 2.1.0a4 (WIP)
 --------------------------
 
 Breaking Changes:
@@ -28,7 +28,8 @@ Bug Fixes:
 ^^^^^^^^^^
 - Relaxed check in logger, that was causing issue on Windows with colorama
 - Fixed off-policy algorithms with continuous float64 actions (see #1145) (@tobirohrer)
-
+- Fixed env_checker.py warning messages for out of bounds in complex observation spaces (@Gabo-Tor)
+
 Deprecations:
 ^^^^^^^^^^^^^
 
@@ -43,6 +44,7 @@ Documentation:
 - Fixed callback example (@BertrandDecoster)
 - Fixed policy network example (@kyle-he)
 - Added mobile-env as new community project (@stefanbschneider)
+- Added [DeepNetSlice](https://github.com/AlexPasqua/DeepNetSlice) to community projects (@AlexPasqua)
 
 
 Release 2.0.0 (2023-06-22)
@@ -1399,7 +1401,7 @@ And all the contributors:
 @eleurent @ac-93 @cove9988 @theDebugger811 @hsuehch @Demetrio92 @thomasgubler @IperGiove @ScheiklP
 @simoninithomas @armandpl @manuel-delverme @Gautam-J @gianlucadecola @buoyancy99 @caburu @xy9485
 @Gregwar @ycheng517 @quantitative-technologies @bcollazo @git-thor @TibiGG @cool-RR @MWeltevrede
-@carlosluis @arjun-kg @tlpss @JonathanKuelz
+@carlosluis @arjun-kg @tlpss @JonathanKuelz @Gabo-Tor
 @Melanol @qgallouedec @francescoluciano @jlp-ue @burakdmb @timothe-chaumont @honglu2875
 @anand-bala @hughperkins @sidney-tio @AlexPasqua @dominicgkerr @Akhilez @Rocamonde @tobirohrer @ZikangXiong
 @DavyMorgan @luizapozzobon @Bonifatius94 @theSquaredError @harveybellini @DavyMorgan @FieteO @jonasreiher @npit @WeberSamuel @troiganto

docs/misc/projects.rst

@@ -212,3 +212,20 @@ It allows simulating various scenarios with moving users in a cellular network w
 | Authors: Stefan Schneider, Stefan Werner
 | Github: https://github.com/stefanbschneider/mobile-env
 | Paper: https://ris.uni-paderborn.de/download/30236/30237 (2022 IEEE/IFIP Network Operations and Management Symposium (NOMS))
+
+
+DeepNetSlice
+------------
+
+A Deep Reinforcement Learning Open-Source Toolkit for Network Slice Placement (NSP).
+
+NSP is the problem of deciding which physical servers in a network should host the virtual network functions (VNFs) that make up a network slice, as well as managing the mapping of the virtual links between the VNFs onto the physical infrastructure.
+It is a complex optimization problem, as it involves considering the requirements of the network slice and the available resources on the physical network.
+The goal is generally to maximize the utilization of the physical resources while ensuring that the network slices meet their performance requirements.
+
+The toolkit includes a customizable simulation environments, as well as some ready-to-use demos for training
+intelligent agents to perform network slice placement.
+
+| Author: Alex Pasquali
+| Github: https://github.com/AlexPasqua/DeepNetSlice
+| Paper: **under review** (citation instructions on the project's README.md) -> see this Master's Thesis for the moment: https://etd.adm.unipi.it/theses/available/etd-01182023-110038/unrestricted/Tesi_magistrale_Pasquali_Alex.pdf

stable_baselines3/common/env_checker.py

@@ -203,18 +203,24 @@ def _check_obs(obs: Union[tuple, dict, np.ndarray, int], observation_space: spac
             f"Expected: {observation_space.dtype}, actual dtype: {obs.dtype}"
         )
         if isinstance(observation_space, spaces.Box):
-            assert np.all(obs >= observation_space.low), (
-                f"The observation returned by the `{method_name}()` method does not match the lower bound "
-                f"of the given observation space {observation_space}."
-                f"Expected: obs >= {np.min(observation_space.low)}, "
-                f"actual min value: {np.min(obs)} at index {np.argmin(obs)}"
-            )
-            assert np.all(obs <= observation_space.high), (
-                f"The observation returned by the `{method_name}()` method does not match the upper bound "
-                f"of the given observation space {observation_space}. "
-                f"Expected: obs <= {np.max(observation_space.high)}, "
-                f"actual max value: {np.max(obs)} at index {np.argmax(obs)}"
-            )
+            lower_bounds, upper_bounds = observation_space.low, observation_space.high
+            # Expose all invalid indices at once
+            invalid_indices = np.where(np.logical_or(obs < lower_bounds, obs > upper_bounds))
+            if (obs > upper_bounds).any() or (obs < lower_bounds).any():
+                message = (
+                    f"The observation returned by the `{method_name}()` method does not match the bounds "
+                    f"of the given observation space {observation_space}. \n"
+                )
+                message += f"{len(invalid_indices[0])} invalid indices: \n"
+
+                for index in zip(*invalid_indices):
+                    index_str = ",".join(map(str, index))
+                    message += (
+                        f"Expected: {lower_bounds[index]} <= obs[{index_str}] <= {upper_bounds[index]}, "
+                        f"actual value: {obs[index]} \n"
+                    )
+
+                raise AssertionError(message)
 
     assert observation_space.contains(obs), (
         f"The observation returned by the `{method_name}()` method "

stable_baselines3/version.txt

@@ -1 +1 @@
-2.1.0a3
+2.1.0a4

tests/test_env_checker.py

@@ -42,15 +42,28 @@ def test_check_env_dict_action():
     [
         # Above upper bound
         (
-            spaces.Box(low=0.0, high=1.0, shape=(3,), dtype=np.float32),
+            spaces.Box(low=np.array([0.0, 0.0, 0.0]), high=np.array([2.0, 1.0, 1.0]), shape=(3,), dtype=np.float32),
             np.array([1.0, 1.5, 0.5], dtype=np.float32),
-            r"Expected: obs <= 1\.0, actual max value: 1\.5 at index 1",
+            r"Expected: 0\.0 <= obs\[1] <= 1\.0, actual value: 1\.5",
+        ),
+        # Above upper bound (multi-dim)
+        (
+            spaces.Box(low=-1.0, high=2.0, shape=(2, 3, 3, 1), dtype=np.float32),
+            3.0 * np.ones((2, 3, 3, 1), dtype=np.float32),
+            # Note: this is one of the 18 invalid indices
+            r"Expected: -1\.0 <= obs\[1,2,1,0\] <= 2\.0, actual value: 3\.0",
         ),
         # Below lower bound
         (
-            spaces.Box(low=0.0, high=2.0, shape=(3,), dtype=np.float32),
+            spaces.Box(low=np.array([0.0, -10.0, 0.0]), high=np.array([2.0, 1.0, 1.0]), shape=(3,), dtype=np.float32),
             np.array([-1.0, 1.5, 0.5], dtype=np.float32),
-            r"Expected: obs >= 0\.0, actual min value: -1\.0 at index 0",
+            r"Expected: 0\.0 <= obs\[0] <= 2\.0, actual value: -1\.0",
+        ),
+        # Below lower bound (multi-dim)
+        (
+            spaces.Box(low=-1.0, high=2.0, shape=(2, 3, 3, 1), dtype=np.float32),
+            -2 * np.ones((2, 3, 3, 1), dtype=np.float32),
+            r"18 invalid indices:",
         ),
         # Wrong dtype
         (
@@ -111,7 +124,7 @@ def step(self, action):
 
     test_env = TestEnv()
     with pytest.raises(AssertionError, match=error_message):
-        check_env(env=test_env)
+        check_env(env=test_env, warn=False)
 
 
 class LimitedStepsTestEnv(gym.Env):