Support black

.github/workflows/build.yml

@@ -19,9 +19,11 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -r requirements.txt
-      - name: check python code format with autopep8
-        run: |
-          autopep8 --diff .
+      - name: check python code format with black
+        uses: psf/black@stable
+        with:
+          options: "--check --verbose"
+          src: "."
   typing:
     runs-on: ubuntu-latest
     timeout-minutes: 3

CONTRIBUTING.md

@@ -69,13 +69,13 @@ $ pip install -e .
 
 ### Code format guidelines
 
-We use [autopep8](https://github.com/hhatto/autopep8) and [isort](https://github.com/PyCQA/isort) to keep consistent coding style. After finishing developing the code, run autopep8 and isort to ensure that your code is correctly formatted.
+We use [black](https://github.com/psf/black) and [isort](https://github.com/PyCQA/isort) to keep consistent coding style. After finishing developing the code, run black and isort to ensure that your code is correctly formatted.
 
-You can run autopep8 and isort as follows.
+You can run black and isort as follows.
 
 ```sh
 cd <nnabla-rl root directory>
-autopep8 .
+black .
 ```
 
 ```sh

README.md

@@ -2,6 +2,7 @@
 [![Build status](https://github.com/sony/nnabla-rl/workflows/Build%20nnabla-rl/badge.svg)](https://github.com/sony/nnabla-rl/actions)
 [![Documentation Status](https://readthedocs.org/projects/nnabla-rl/badge/?version=latest)](https://nnabla-rl.readthedocs.io/en/latest/?badge=latest)
 [![Doc style](https://img.shields.io/badge/%20style-google-3666d6.svg)](https://google.github.io/styleguide/pyguide.html#s3.8-comments-and-docstrings)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
 # Deep Reinforcement Learning Library built on top of Neural Network Libraries
 

docs/source/conf.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -18,14 +18,14 @@
 
 import sphinx_rtd_theme
 
-sys.path.insert(0, os.path.abspath('../../'))
+sys.path.insert(0, os.path.abspath("../../"))
 
 import nnabla_rl  # noqa
 
 # -- Project information -----------------------------------------------------
-project = 'nnablaRL'
-copyright = '2021, Sony Group Corporation'
-author = 'Sony Group Corporation'
+project = "nnablaRL"
+copyright = "2021, Sony Group Corporation"
+author = "Sony Group Corporation"
 release = nnabla_rl.__version__
 
 # -- General configuration ---------------------------------------------------
@@ -34,22 +34,22 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    'sphinx.ext.doctest',
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
-    'sphinx.ext.mathjax',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.viewcode'
+    "sphinx.ext.doctest",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.todo",
+    "sphinx.ext.mathjax",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.viewcode",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['ntemplates']
+templates_path = ["ntemplates"]
 
-source_suffix = '.rst'
+source_suffix = ".rst"
 # The master toctree document.
-master_doc = 'index'
+master_doc = "index"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

examples/evaluate_trained_model.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021,2022,2023 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -22,16 +22,16 @@
 
 def build_env():
     try:
-        env = gym.make('Pendulum-v0')
+        env = gym.make("Pendulum-v0")
     except gym.error.DeprecatedEnv:
-        env = gym.make('Pendulum-v1')
+        env = gym.make("Pendulum-v1")
     env = NumpyFloat32Env(env)
     env = ScreenRenderEnv(env)
     return env
 
 
 def main():
-    snapshot_dir = './pendulum_v0_snapshot/iteration-10000'
+    snapshot_dir = "./pendulum_v0_snapshot/iteration-10000"
     env = build_env()
 
     algorithm = serializers.load_snapshot(snapshot_dir, env)

examples/hook_example.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -24,15 +24,15 @@ def __init__(self):
         super().__init__(timing=1)
 
     def on_hook_called(self, algorithm):
-        print('hello!!')
+        print("hello!!")
 
 
 class PrintOnlyEvenIteraion(Hook):
     def __init__(self):
         super().__init__(timing=2)
 
     def on_hook_called(self, algorithm):
-        print('even iteration -> {}'.format(algorithm.iteration_num))
+        print("even iteration -> {}".format(algorithm.iteration_num))
 
 
 def main():

examples/recurrent_model.py

@@ -1,4 +1,4 @@
-# Copyright 2021 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -65,17 +65,17 @@ def internal_state_shapes(self) -> Dict[str, Tuple[int, ...]]:
         shapes: Dict[str, nn.Variable] = {}
         # You can use arbitral (but distinguishable) key as name
         # Use same key for same state
-        shapes['my_lstm_h'] = (self._lstm_state_size, )
-        shapes['my_lstm_c'] = (self._lstm_state_size, )
+        shapes["my_lstm_h"] = (self._lstm_state_size,)
+        shapes["my_lstm_c"] = (self._lstm_state_size,)
         return shapes
 
     def get_internal_states(self) -> Dict[str, nn.Variable]:
         # Return current internal states
         states: Dict[str, nn.Variable] = {}
         # You can use arbitral (but distinguishable) key as name
         # Use same key for same state.
-        states['my_lstm_h'] = self._h
-        states['my_lstm_c'] = self._c
+        states["my_lstm_h"] = self._h
+        states["my_lstm_c"] = self._c
         return states
 
     def set_internal_states(self, states: Optional[Dict[str, nn.Variable]] = None):
@@ -88,8 +88,8 @@ def set_internal_states(self, states: Optional[Dict[str, nn.Variable]] = None):
         else:
             # Otherwise, set given states
             # Use the key defined in internal_state_shapes() for getting the states
-            self._h = states['my_lstm_h']
-            self._c = states['my_lstm_c']
+            self._h = states["my_lstm_h"]
+            self._c = states["my_lstm_c"]
 
     def _create_internal_states(self, batch_size):
         self._h = nn.Variable((batch_size, self._lstm_state_size))
@@ -103,11 +103,9 @@ def _is_internal_state_created(self) -> bool:
 
 
 class QFunctionWithRNNBuilder(ModelBuilder[QFunction]):
-    def build_model(self,
-                    scope_name: str,
-                    env_info: EnvironmentInfo,
-                    algorithm_config: AlgorithmConfig,
-                    **kwargs) -> QFunction:
+    def build_model(
+        self, scope_name: str, env_info: EnvironmentInfo, algorithm_config: AlgorithmConfig, **kwargs
+    ) -> QFunction:
         action_num = env_info.action_dim
         return QFunctionWithRNN(scope_name, action_num)
 
@@ -118,7 +116,7 @@ def build_solver(self, env_info: EnvironmentInfo, algorithm_config: AlgorithmCon
 
 
 def build_env(seed=None):
-    env = gym.make('MountainCar-v0')
+    env = gym.make("MountainCar-v0")
     env = NumpyFloat32Env(env)
     env = ScreenRenderEnv(env)
     env.seed(seed)
@@ -133,30 +131,32 @@ def main():
     # Here, we use DRQN algorithm
     # For the list of algorithms that support RNN layers see
     # https://github.com/sony/nnabla-rl/tree/master/nnabla_rl/algorithms
-    config = A.DRQNConfig(gpu_id=0,
-                          learning_rate=1e-2,
-                          gamma=0.9,
-                          learner_update_frequency=1,
-                          target_update_frequency=200,
-                          start_timesteps=200,
-                          replay_buffer_size=10000,
-                          max_explore_steps=10000,
-                          initial_epsilon=1.0,
-                          final_epsilon=0.001,
-                          test_epsilon=0.001,
-                          grad_clip=None,
-                          unroll_steps=2)  # Unroll only for 2 timesteps for fast iteration. Because this is an example
-    drqn = A.DRQN(train_env,
-                  config=config,
-                  q_func_builder=QFunctionWithRNNBuilder(),
-                  q_solver_builder=AdamSolverBuilder())
+    config = A.DRQNConfig(
+        gpu_id=0,
+        learning_rate=1e-2,
+        gamma=0.9,
+        learner_update_frequency=1,
+        target_update_frequency=200,
+        start_timesteps=200,
+        replay_buffer_size=10000,
+        max_explore_steps=10000,
+        initial_epsilon=1.0,
+        final_epsilon=0.001,
+        test_epsilon=0.001,
+        grad_clip=None,
+        unroll_steps=2,
+    )  # Unroll only for 2 timesteps for fast iteration. Because this is an example
+    drqn = A.DRQN(
+        train_env, config=config, q_func_builder=QFunctionWithRNNBuilder(), q_solver_builder=AdamSolverBuilder()
+    )
 
     # Optional: Add hooks to check the training progress
     eval_env = build_env(seed=100)
     evaluation_hook = H.EvaluationHook(
         eval_env,
         timing=1000,
-        writer=W.FileWriter(outdir='./mountain_car_v0_drqn_results', file_prefix='evaluation_result'))
+        writer=W.FileWriter(outdir="./mountain_car_v0_drqn_results", file_prefix="evaluation_result"),
+    )
     iteration_num_hook = H.IterationNumHook(timing=100)
     drqn.set_hooks(hooks=[iteration_num_hook, evaluation_hook])
 

examples/rl_project_template/environment.py

@@ -1,4 +1,4 @@
-# Copyright 2023 Sony Group Corporation.
+# Copyright 2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -20,15 +20,15 @@ def __init__(self, max_episode_steps=100):
         # max_episode_steps is the maximum possible steps that the rl agent interacts with this environment.
         # You can set this value to None if the is no limits.
         # The first argument is the name of this environment used when registering this environment to gym.
-        self.spec = EnvSpec('template-v0', max_episode_steps=max_episode_steps)
+        self.spec = EnvSpec("template-v0", max_episode_steps=max_episode_steps)
         self._episode_steps = 0
 
         # Use gym's spaces to define the shapes and ranges of states and actions.
         # observation_space: definition of states's shape and its ranges
         # action_space: definition of actions's shape and its ranges
-        observation_shape = (10, )  # Example 10 dimensional state with range of [0.0, 1.0] each.
+        observation_shape = (10,)  # Example 10 dimensional state with range of [0.0, 1.0] each.
         self.observation_space = gym.spaces.Box(low=0.0, high=1.0, shape=observation_shape)
-        action_shape = (1, )  # 1 dimensional continuous action with range of [0.0, 1.0].
+        action_shape = (1,)  # 1 dimensional continuous action with range of [0.0, 1.0].
         self.action_space = gym.spaces.Box(low=0.0, high=1.0, shape=action_shape)
 
     def reset(self):

examples/rl_project_template/models.py

@@ -1,4 +1,4 @@
-# Copyright 2023 Sony Group Corporation.
+# Copyright 2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -58,7 +58,7 @@ def pi(self, state: nn.Variable) -> Distribution:
             h = NF.relu(x=h)
             h = NPF.affine(h, n_outmaps=256, name="linear2")
             h = NF.relu(x=h)
-            h = NPF.affine(h, n_outmaps=self._action_dim*2, name="linear3")
+            h = NPF.affine(h, n_outmaps=self._action_dim * 2, name="linear3")
             reshaped = NF.reshape(h, shape=(-1, 2, self._action_dim))
 
             # Split the output into mean and variance of the Gaussian distribution.

examples/rl_project_template/training.py

@@ -1,4 +1,4 @@
-# Copyright 2023 Sony Group Corporation.
+# Copyright 2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -75,7 +75,7 @@ def run_training(args):
     # Save trained parameters every "timing" steps.
     # Without this, the parameters will not be saved.
     # We recommend saving parameters at every evaluation timing.
-    outdir = pathlib.Path(args.save_dir) / 'snapshots'
+    outdir = pathlib.Path(args.save_dir) / "snapshots"
     save_snapshot_hook = H.SaveSnapshotHook(outdir=outdir, timing=1000)
 
     # All instantiated hooks should be set at once.
@@ -87,13 +87,13 @@ def run_training(args):
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--gpu', type=int, default=-1)
-    parser.add_argument('--save-dir', type=str, default=str(pathlib.Path(__file__).parent))
-    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument("--gpu", type=int, default=-1)
+    parser.add_argument("--save-dir", type=str, default=str(pathlib.Path(__file__).parent))
+    parser.add_argument("--seed", type=int, default=0)
     args = parser.parse_args()
 
     run_training(args)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

examples/save_load_snapshot.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -23,7 +23,7 @@ def main():
     env = E.DummyContinuous()
     ddpg = A.DDPG(env, config=config)
 
-    outdir = './save_load_snapshot'
+    outdir = "./save_load_snapshot"
 
     # This actually saves the model and solver state right after the algorithm construction
     snapshot_dir = serializers.save_snapshot(outdir, ddpg)

examples/training_example.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021,2022,2023 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -23,9 +23,9 @@
 
 def build_env(seed=None):
     try:
-        env = gym.make('Pendulum-v0')
+        env = gym.make("Pendulum-v0")
     except gym.error.DeprecatedEnv:
-        env = gym.make('Pendulum-v1')
+        env = gym.make("Pendulum-v1")
     env = NumpyFloat32Env(env)
     env = ScreenRenderEnv(env)
     env.seed(seed)
@@ -36,15 +36,14 @@ def main():
     # Evaluate the trained network (Optional)
     eval_env = build_env(seed=100)
     evaluation_hook = H.EvaluationHook(
-        eval_env,
-        timing=1000,
-        writer=W.FileWriter(outdir='./pendulum_v0_ddpg_results', file_prefix='evaluation_result'))
+        eval_env, timing=1000, writer=W.FileWriter(outdir="./pendulum_v0_ddpg_results", file_prefix="evaluation_result")
+    )
 
     # Pring iteration number every 100 iterations.
     iteration_num_hook = H.IterationNumHook(timing=100)
 
     # Save trained algorithm snapshot (Optional)
-    save_snapshot_hook = H.SaveSnapshotHook('./pendulum_v0_ddpg_results', timing=1000)
+    save_snapshot_hook = H.SaveSnapshotHook("./pendulum_v0_ddpg_results", timing=1000)
 
     train_env = build_env()
     config = A.DDPGConfig(start_timesteps=200, gpu_id=0)