Release v1.6.0 and bug fix for TRPO (#84)

docs/conda_env.yml

@@ -6,9 +6,9 @@ dependencies:
   - cpuonly=1.0=0
   - pip=21.1
   - python=3.7
-  - pytorch=1.8.1=py3.7_cpu_0
+  - pytorch=1.11=py3.7_cpu_0
   - pip:
-    - gym>=0.17.2
+    - gym==0.21
     - cloudpickle
     - opencv-python-headless
     - pandas
@@ -17,5 +17,5 @@ dependencies:
     - sphinx_autodoc_typehints
     - stable-baselines3>=1.3.0
     - sphinx>=4.2
-    # See https://github.com/readthedocs/sphinx_rtd_theme/issues/1115
     - sphinx_rtd_theme>=1.0
+    - sphinx_copybutton

docs/conf.py

@@ -24,6 +24,14 @@
 except ImportError:
     enable_spell_check = False
 
+# Try to enable copy button
+try:
+    import sphinx_copybutton  # noqa: F401
+
+    enable_copy_button = True
+except ImportError:
+    enable_copy_button = False
+
 # source code directory, relative to this file, for sphinx-autobuild
 sys.path.insert(0, os.path.abspath(".."))
 
@@ -51,7 +59,7 @@ def __getattr__(cls, name):
 # -- Project information -----------------------------------------------------
 
 project = "Stable Baselines3 - Contrib"
-copyright = "2020, Stable Baselines3"
+copyright = "2022, Stable Baselines3"
 author = "Stable Baselines3 Contributors"
 
 # The short X.Y version
@@ -83,6 +91,9 @@ def __getattr__(cls, name):
 if enable_spell_check:
     extensions.append("sphinxcontrib.spelling")
 
+if enable_copy_button:
+    extensions.append("sphinx_copybutton")
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 

docs/misc/changelog.rst

@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 1.5.1a9 (WIP)
+Release 1.6.0 (2022-07-11)
 -------------------------------
 
 **Add RecurrentPPO (aka PPO LSTM)**
@@ -25,8 +25,9 @@ New Features:
 - Added ``RecurrentPPO`` (aka PPO LSTM)
 
 Bug Fixes:
-- Fixed a bug in ``RecurrentPPO`` when calculating the masked loss functions (@rnederstigt)
 ^^^^^^^^^^
+- Fixed a bug in ``RecurrentPPO`` when calculating the masked loss functions (@rnederstigt)
+- Fixed a bug in ``TRPO`` where kl divergence was not implemented for ``MultiDiscrete`` space
 
 Deprecations:
 ^^^^^^^^^^^^^

sb3_contrib/trpo/trpo.py

@@ -6,12 +6,12 @@
 import numpy as np
 import torch as th
 from gym import spaces
+from stable_baselines3.common.distributions import kl_divergence
 from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
 from stable_baselines3.common.policies import ActorCriticPolicy, BasePolicy
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, RolloutBufferSamples, Schedule
 from stable_baselines3.common.utils import explained_variance
 from torch import nn
-from torch.distributions import kl_divergence
 from torch.nn import functional as F
 
 from sb3_contrib.common.utils import conjugate_gradient_solver, flat_grad
@@ -279,7 +279,7 @@ def train(self) -> None:
             policy_objective = (advantages * ratio).mean()
 
             # KL divergence
-            kl_div = kl_divergence(distribution.distribution, old_distribution.distribution).mean()
+            kl_div = kl_divergence(distribution, old_distribution).mean()
 
             # Surrogate & KL gradient
             self.policy.optimizer.zero_grad()
@@ -332,7 +332,7 @@ def train(self) -> None:
                     new_policy_objective = (advantages * ratio).mean()
 
                     # New KL-divergence
-                    kl_div = kl_divergence(distribution.distribution, old_distribution.distribution).mean()
+                    kl_div = kl_divergence(distribution, old_distribution).mean()
 
                     # Constraint criteria:
                     # we need to improve the surrogate policy objective

sb3_contrib/version.txt

@@ -1 +1 @@
-1.5.1a9
+1.6.0

setup.py

@@ -65,7 +65,7 @@
     packages=[package for package in find_packages() if package.startswith("sb3_contrib")],
     package_data={"sb3_contrib": ["py.typed", "version.txt"]},
     install_requires=[
-        "stable_baselines3>=1.5.1a7",
+        "stable_baselines3>=1.6.0",
     ],
     description="Contrib package of Stable Baselines3, experimental code.",
     author="Antonin Raffin",

tests/test_identity.py

@@ -0,0 +1,38 @@
+import numpy as np
+import pytest
+from stable_baselines3.common.envs import IdentityEnv, IdentityEnvMultiBinary, IdentityEnvMultiDiscrete
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.vec_env import DummyVecEnv
+
+from sb3_contrib import QRDQN, TRPO
+
+DIM = 4
+
+
+@pytest.mark.parametrize("model_class", [QRDQN, TRPO])
+@pytest.mark.parametrize("env", [IdentityEnv(DIM), IdentityEnvMultiDiscrete(DIM), IdentityEnvMultiBinary(DIM)])
+def test_discrete(model_class, env):
+    env_ = DummyVecEnv([lambda: env])
+    kwargs = {}
+    n_steps = 1500
+    if model_class == QRDQN:
+        kwargs = dict(
+            learning_starts=0,
+            policy_kwargs=dict(n_quantiles=25, net_arch=[32]),
+            target_update_interval=10,
+            train_freq=2,
+            batch_size=256,
+        )
+        n_steps = 1500
+        # DQN only support discrete actions
+        if isinstance(env, (IdentityEnvMultiDiscrete, IdentityEnvMultiBinary)):
+            return
+    elif n_steps == TRPO:
+        kwargs = dict(n_steps=256, cg_max_steps=5)
+
+    model = model_class("MlpPolicy", env_, learning_rate=1e-3, gamma=0.4, seed=1, **kwargs).learn(n_steps)
+
+    evaluate_policy(model, env_, n_eval_episodes=20, reward_threshold=90, warn=False)
+    obs = env.reset()
+
+    assert np.shape(model.predict(obs)[0]) == np.shape(obs)