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+%global _empty_manifest_terminate_build 0
+Name:		python-pfrl
+Version:	0.3.0
+Release:	1
+Summary:	PFRL, a deep reinforcement learning library
+License:	MIT License
+URL:		https://pypi.org/project/pfrl/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/1c/55/053cdd48aee18a4a5d9ae5ca864ff50f7294da0f12a01cdff9f86ea4496b/pfrl-0.3.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+<div align="center"><img src="https://raw.githubusercontent.com/pfnet/pfrl/master/assets/PFRL.png" height=150/></div>
+
+# PFRL
+[![Documentation Status](https://readthedocs.org/projects/pfrl/badge/?version=latest)](http://pfrl.readthedocs.io/en/latest/?badge=latest)
+[![PyPI](https://img.shields.io/pypi/v/pfrl.svg)](https://pypi.python.org/pypi/pfrl)
+
+PFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using [PyTorch](https://github.com/pytorch/pytorch).
+
+![Boxing](assets/boxing.gif)
+![Humanoid](assets/humanoid.gif)
+![Grasping](assets/grasping.gif)
+![Atlas](examples/atlas/assets/atlas.gif)
+![SlimeVolley](examples/slimevolley/assets/slimevolley.gif)
+
+## Installation
+
+PFRL is tested with Python 3.7.7. For other requirements, see [requirements.txt](requirements.txt).
+
+PFRL can be installed via PyPI:
+```
+pip install pfrl
+```
+
+It can also be installed from the source code:
+```
+python setup.py install
+```
+
+Refer to [Installation](http://pfrl.readthedocs.io/en/latest/install.html) for more information on installation. 
+
+## Getting started
+
+You can try [PFRL Quickstart Guide](examples/quickstart/quickstart.ipynb) first, or check the [examples](examples) ready for Atari 2600 and Open AI Gym.
+
+For more information, you can refer to [PFRL's documentation](http://pfrl.readthedocs.io/en/latest/index.html).
+
+### Blog Posts
+- [Introducing PFRL: A PyTorch-based Deep RL Library](https://t.co/VaT06nejSC?amp=1)
+- [PFRL’s Pretrained Model Zoo](https://bit.ly/3fNx5xH)
+
+## Algorithms
+
+| Algorithm | Discrete Action | Continous Action | Recurrent Model | Batch Training | CPU Async Training | Pretrained models<sup>*</sup> |
+|:----------|:---------------:|:----------------:|:---------------:|:--------------:|:------------------:|:------------------:|
+| DQN (including DoubleDQN etc.) | ✓ | ✓ (NAF) | ✓ | ✓ | x | ✓ |
+| Categorical DQN | ✓ | x | ✓ | ✓ | x | x |
+| Rainbow | ✓ | x | ✓ | ✓ | x | ✓ |
+| IQN | ✓ | x | ✓ | ✓ | x | ✓ |
+| DDPG | x | ✓ | x | ✓ | x | ✓ |
+| A3C  | ✓ | ✓ | ✓ | ✓ (A2C) | ✓ | ✓ |
+| ACER | ✓ | ✓ | ✓ | x | ✓ | x |
+| PPO  | ✓ | ✓ | ✓ | ✓ | x | ✓ |
+| TRPO | ✓ | ✓ | ✓ | ✓ | x | ✓ |
+| TD3 | x | ✓ | x | ✓ | x | ✓ |
+| SAC | x | ✓ | x | ✓ | x | ✓ |
+
+**<sup>*</sup>Note on Pretrained models**: PFRL provides pretrained models (sometimes called a 'model zoo') for our reproducibility scripts on [Atari environments](https://github.com/pfnet/pfrl/tree/master/examples/atari/reproduction) (DQN, IQN, Rainbow, and A3C) and [Mujoco environments](https://github.com/pfnet/pfrl/tree/master/examples/mujoco/reproduction) (DDPG, TRPO, PPO, TD3, SAC), for each benchmarked environment. 
+
+Following algorithms have been implemented in PFRL:
+- [A2C (Synchronous variant of A3C)](https://openai.com/blog/baselines-acktr-a2c/)
+  - examples: [[atari (batched)]](examples/atari/train_a2c_ale.py)
+- [A3C (Asynchronous Advantage Actor-Critic)](https://arxiv.org/abs/1602.01783)
+  - examples: [[atari reproduction]](examples/atari/reproduction/a3c) [[atari]](examples/atari/train_a3c_ale.py)
+- [ACER (Actor-Critic with Experience Replay)](https://arxiv.org/abs/1611.01224)
+  - examples: [[atari]](examples/atari/train_acer_ale.py)
+- [Categorical DQN](https://arxiv.org/abs/1707.06887)
+  - examples: [[atari]](examples/atari/train_categorical_dqn_ale.py) [[general gym]](examples/gym/train_categorical_dqn_gym.py)
+- [DQN (Deep Q-Network)](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) (including [Double DQN](https://arxiv.org/abs/1509.06461), [Persistent Advantage Learning (PAL)](https://arxiv.org/abs/1512.04860), Double PAL, [Dynamic Policy Programming (DPP)](http://www.jmlr.org/papers/volume13/azar12a/azar12a.pdf))
+  - examples: [[atari reproduction]](examples/atari/reproduction/dqn) [[atari]](examples/atari/train_dqn_ale.py) [[atari (batched)]](examples/atari/train_dqn_batch_ale.py) [[flickering atari]](examples/atari/train_drqn_ale.py) [[general gym]](examples/gym/train_dqn_gym.py)
+- [DDPG (Deep Deterministic Policy Gradients)](https://arxiv.org/abs/1509.02971) (including [SVG(0)](https://arxiv.org/abs/1510.09142))
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/ddpg)
+- [IQN (Implicit Quantile Networks)](https://arxiv.org/abs/1806.06923)
+  - examples: [[atari reproduction]](examples/atari/reproduction/iqn)
+- [PPO (Proximal Policy Optimization)](https://arxiv.org/abs/1707.06347)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/ppo) [[atari]](examples/atari/train_ppo_ale.py)
+- [Rainbow](https://arxiv.org/abs/1710.02298)
+  - examples: [[atari reproduction]](examples/atari/reproduction/rainbow) [[Slime volleyball]](examples/slimevolley/)
+- [REINFORCE](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)
+  - examples: [[general gym]](examples/gym/train_reinforce_gym.py)
+- [SAC (Soft Actor-Critic)](https://arxiv.org/abs/1812.05905)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/soft_actor_critic) [[Atlas walk]](examples/atlas/)
+- [TRPO (Trust Region Policy Optimization)](https://arxiv.org/abs/1502.05477) with [GAE (Generalized Advantage Estimation)](https://arxiv.org/abs/1506.02438)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/trpo)
+- [TD3 (Twin Delayed Deep Deterministic policy gradient algorithm)](https://arxiv.org/abs/1802.09477)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/td3)
+
+Following useful techniques have been also implemented in PFRL:
+- [NoisyNet](https://arxiv.org/abs/1706.10295)
+  - examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
+- [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)
+  - examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
+- [Dueling Network](https://arxiv.org/abs/1511.06581)
+  - examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
+- [Normalized Advantage Function](https://arxiv.org/abs/1603.00748)
+  - examples: [[DQN]](examples/gym/train_dqn_gym.py) (for continuous-action envs only)
+- [Deep Recurrent Q-Network](https://arxiv.org/abs/1507.06527)
+  - examples: [[DQN]](examples/atari/train_drqn_ale.py)
+
+
+## Environments
+
+Environments that support the subset of OpenAI Gym's interface (`reset` and `step` methods) can be used.
+
+## Contributing
+
+Any kind of contribution to PFRL would be highly appreciated! If you are interested in contributing to PFRL, please read [CONTRIBUTING.md](CONTRIBUTING.md).
+
+## License
+
+[MIT License](LICENSE).
+
+## Citations
+
+To cite PFRL in publications, please cite our [paper](https://www.jmlr.org/papers/v22/20-376.html) on ChainerRL, the library on which PFRL is based:
+
+```
+@article{JMLR:v22:20-376,
+  author  = {Yasuhiro Fujita and Prabhat Nagarajan and Toshiki Kataoka and Takahiro Ishikawa},
+  title   = {ChainerRL: A Deep Reinforcement Learning Library},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {77},
+  pages   = {1-14},
+  url     = {http://jmlr.org/papers/v22/20-376.html}
+}
+```
+
+%package -n python3-pfrl
+Summary:	PFRL, a deep reinforcement learning library
+Provides:	python-pfrl
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pfrl
+<div align="center"><img src="https://raw.githubusercontent.com/pfnet/pfrl/master/assets/PFRL.png" height=150/></div>
+
+# PFRL
+[![Documentation Status](https://readthedocs.org/projects/pfrl/badge/?version=latest)](http://pfrl.readthedocs.io/en/latest/?badge=latest)
+[![PyPI](https://img.shields.io/pypi/v/pfrl.svg)](https://pypi.python.org/pypi/pfrl)
+
+PFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using [PyTorch](https://github.com/pytorch/pytorch).
+
+![Boxing](assets/boxing.gif)
+![Humanoid](assets/humanoid.gif)
+![Grasping](assets/grasping.gif)
+![Atlas](examples/atlas/assets/atlas.gif)
+![SlimeVolley](examples/slimevolley/assets/slimevolley.gif)
+
+## Installation
+
+PFRL is tested with Python 3.7.7. For other requirements, see [requirements.txt](requirements.txt).
+
+PFRL can be installed via PyPI:
+```
+pip install pfrl
+```
+
+It can also be installed from the source code:
+```
+python setup.py install
+```
+
+Refer to [Installation](http://pfrl.readthedocs.io/en/latest/install.html) for more information on installation. 
+
+## Getting started
+
+You can try [PFRL Quickstart Guide](examples/quickstart/quickstart.ipynb) first, or check the [examples](examples) ready for Atari 2600 and Open AI Gym.
+
+For more information, you can refer to [PFRL's documentation](http://pfrl.readthedocs.io/en/latest/index.html).
+
+### Blog Posts
+- [Introducing PFRL: A PyTorch-based Deep RL Library](https://t.co/VaT06nejSC?amp=1)
+- [PFRL’s Pretrained Model Zoo](https://bit.ly/3fNx5xH)
+
+## Algorithms
+
+| Algorithm | Discrete Action | Continous Action | Recurrent Model | Batch Training | CPU Async Training | Pretrained models<sup>*</sup> |
+|:----------|:---------------:|:----------------:|:---------------:|:--------------:|:------------------:|:------------------:|
+| DQN (including DoubleDQN etc.) | ✓ | ✓ (NAF) | ✓ | ✓ | x | ✓ |
+| Categorical DQN | ✓ | x | ✓ | ✓ | x | x |
+| Rainbow | ✓ | x | ✓ | ✓ | x | ✓ |
+| IQN | ✓ | x | ✓ | ✓ | x | ✓ |
+| DDPG | x | ✓ | x | ✓ | x | ✓ |
+| A3C  | ✓ | ✓ | ✓ | ✓ (A2C) | ✓ | ✓ |
+| ACER | ✓ | ✓ | ✓ | x | ✓ | x |
+| PPO  | ✓ | ✓ | ✓ | ✓ | x | ✓ |
+| TRPO | ✓ | ✓ | ✓ | ✓ | x | ✓ |
+| TD3 | x | ✓ | x | ✓ | x | ✓ |
+| SAC | x | ✓ | x | ✓ | x | ✓ |
+
+**<sup>*</sup>Note on Pretrained models**: PFRL provides pretrained models (sometimes called a 'model zoo') for our reproducibility scripts on [Atari environments](https://github.com/pfnet/pfrl/tree/master/examples/atari/reproduction) (DQN, IQN, Rainbow, and A3C) and [Mujoco environments](https://github.com/pfnet/pfrl/tree/master/examples/mujoco/reproduction) (DDPG, TRPO, PPO, TD3, SAC), for each benchmarked environment. 
+
+Following algorithms have been implemented in PFRL:
+- [A2C (Synchronous variant of A3C)](https://openai.com/blog/baselines-acktr-a2c/)
+  - examples: [[atari (batched)]](examples/atari/train_a2c_ale.py)
+- [A3C (Asynchronous Advantage Actor-Critic)](https://arxiv.org/abs/1602.01783)
+  - examples: [[atari reproduction]](examples/atari/reproduction/a3c) [[atari]](examples/atari/train_a3c_ale.py)
+- [ACER (Actor-Critic with Experience Replay)](https://arxiv.org/abs/1611.01224)
+  - examples: [[atari]](examples/atari/train_acer_ale.py)
+- [Categorical DQN](https://arxiv.org/abs/1707.06887)
+  - examples: [[atari]](examples/atari/train_categorical_dqn_ale.py) [[general gym]](examples/gym/train_categorical_dqn_gym.py)
+- [DQN (Deep Q-Network)](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) (including [Double DQN](https://arxiv.org/abs/1509.06461), [Persistent Advantage Learning (PAL)](https://arxiv.org/abs/1512.04860), Double PAL, [Dynamic Policy Programming (DPP)](http://www.jmlr.org/papers/volume13/azar12a/azar12a.pdf))
+  - examples: [[atari reproduction]](examples/atari/reproduction/dqn) [[atari]](examples/atari/train_dqn_ale.py) [[atari (batched)]](examples/atari/train_dqn_batch_ale.py) [[flickering atari]](examples/atari/train_drqn_ale.py) [[general gym]](examples/gym/train_dqn_gym.py)
+- [DDPG (Deep Deterministic Policy Gradients)](https://arxiv.org/abs/1509.02971) (including [SVG(0)](https://arxiv.org/abs/1510.09142))
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/ddpg)
+- [IQN (Implicit Quantile Networks)](https://arxiv.org/abs/1806.06923)
+  - examples: [[atari reproduction]](examples/atari/reproduction/iqn)
+- [PPO (Proximal Policy Optimization)](https://arxiv.org/abs/1707.06347)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/ppo) [[atari]](examples/atari/train_ppo_ale.py)
+- [Rainbow](https://arxiv.org/abs/1710.02298)
+  - examples: [[atari reproduction]](examples/atari/reproduction/rainbow) [[Slime volleyball]](examples/slimevolley/)
+- [REINFORCE](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)
+  - examples: [[general gym]](examples/gym/train_reinforce_gym.py)
+- [SAC (Soft Actor-Critic)](https://arxiv.org/abs/1812.05905)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/soft_actor_critic) [[Atlas walk]](examples/atlas/)
+- [TRPO (Trust Region Policy Optimization)](https://arxiv.org/abs/1502.05477) with [GAE (Generalized Advantage Estimation)](https://arxiv.org/abs/1506.02438)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/trpo)
+- [TD3 (Twin Delayed Deep Deterministic policy gradient algorithm)](https://arxiv.org/abs/1802.09477)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/td3)
+
+Following useful techniques have been also implemented in PFRL:
+- [NoisyNet](https://arxiv.org/abs/1706.10295)
+  - examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
+- [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)
+  - examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
+- [Dueling Network](https://arxiv.org/abs/1511.06581)
+  - examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
+- [Normalized Advantage Function](https://arxiv.org/abs/1603.00748)
+  - examples: [[DQN]](examples/gym/train_dqn_gym.py) (for continuous-action envs only)
+- [Deep Recurrent Q-Network](https://arxiv.org/abs/1507.06527)
+  - examples: [[DQN]](examples/atari/train_drqn_ale.py)
+
+
+## Environments
+
+Environments that support the subset of OpenAI Gym's interface (`reset` and `step` methods) can be used.
+
+## Contributing
+
+Any kind of contribution to PFRL would be highly appreciated! If you are interested in contributing to PFRL, please read [CONTRIBUTING.md](CONTRIBUTING.md).
+
+## License
+
+[MIT License](LICENSE).
+
+## Citations
+
+To cite PFRL in publications, please cite our [paper](https://www.jmlr.org/papers/v22/20-376.html) on ChainerRL, the library on which PFRL is based:
+
+```
+@article{JMLR:v22:20-376,
+  author  = {Yasuhiro Fujita and Prabhat Nagarajan and Toshiki Kataoka and Takahiro Ishikawa},
+  title   = {ChainerRL: A Deep Reinforcement Learning Library},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {77},
+  pages   = {1-14},
+  url     = {http://jmlr.org/papers/v22/20-376.html}
+}
+```
+
+%package help
+Summary:	Development documents and examples for pfrl
+Provides:	python3-pfrl-doc
+%description help
+<div align="center"><img src="https://raw.githubusercontent.com/pfnet/pfrl/master/assets/PFRL.png" height=150/></div>
+
+# PFRL
+[![Documentation Status](https://readthedocs.org/projects/pfrl/badge/?version=latest)](http://pfrl.readthedocs.io/en/latest/?badge=latest)
+[![PyPI](https://img.shields.io/pypi/v/pfrl.svg)](https://pypi.python.org/pypi/pfrl)
+
+PFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using [PyTorch](https://github.com/pytorch/pytorch).
+
+![Boxing](assets/boxing.gif)
+![Humanoid](assets/humanoid.gif)
+![Grasping](assets/grasping.gif)
+![Atlas](examples/atlas/assets/atlas.gif)
+![SlimeVolley](examples/slimevolley/assets/slimevolley.gif)
+
+## Installation
+
+PFRL is tested with Python 3.7.7. For other requirements, see [requirements.txt](requirements.txt).
+
+PFRL can be installed via PyPI:
+```
+pip install pfrl
+```
+
+It can also be installed from the source code:
+```
+python setup.py install
+```
+
+Refer to [Installation](http://pfrl.readthedocs.io/en/latest/install.html) for more information on installation. 
+
+## Getting started
+
+You can try [PFRL Quickstart Guide](examples/quickstart/quickstart.ipynb) first, or check the [examples](examples) ready for Atari 2600 and Open AI Gym.
+
+For more information, you can refer to [PFRL's documentation](http://pfrl.readthedocs.io/en/latest/index.html).
+
+### Blog Posts
+- [Introducing PFRL: A PyTorch-based Deep RL Library](https://t.co/VaT06nejSC?amp=1)
+- [PFRL’s Pretrained Model Zoo](https://bit.ly/3fNx5xH)
+
+## Algorithms
+
+| Algorithm | Discrete Action | Continous Action | Recurrent Model | Batch Training | CPU Async Training | Pretrained models<sup>*</sup> |
+|:----------|:---------------:|:----------------:|:---------------:|:--------------:|:------------------:|:------------------:|
+| DQN (including DoubleDQN etc.) | ✓ | ✓ (NAF) | ✓ | ✓ | x | ✓ |
+| Categorical DQN | ✓ | x | ✓ | ✓ | x | x |
+| Rainbow | ✓ | x | ✓ | ✓ | x | ✓ |
+| IQN | ✓ | x | ✓ | ✓ | x | ✓ |
+| DDPG | x | ✓ | x | ✓ | x | ✓ |
+| A3C  | ✓ | ✓ | ✓ | ✓ (A2C) | ✓ | ✓ |
+| ACER | ✓ | ✓ | ✓ | x | ✓ | x |
+| PPO  | ✓ | ✓ | ✓ | ✓ | x | ✓ |
+| TRPO | ✓ | ✓ | ✓ | ✓ | x | ✓ |
+| TD3 | x | ✓ | x | ✓ | x | ✓ |
+| SAC | x | ✓ | x | ✓ | x | ✓ |
+
+**<sup>*</sup>Note on Pretrained models**: PFRL provides pretrained models (sometimes called a 'model zoo') for our reproducibility scripts on [Atari environments](https://github.com/pfnet/pfrl/tree/master/examples/atari/reproduction) (DQN, IQN, Rainbow, and A3C) and [Mujoco environments](https://github.com/pfnet/pfrl/tree/master/examples/mujoco/reproduction) (DDPG, TRPO, PPO, TD3, SAC), for each benchmarked environment. 
+
+Following algorithms have been implemented in PFRL:
+- [A2C (Synchronous variant of A3C)](https://openai.com/blog/baselines-acktr-a2c/)
+  - examples: [[atari (batched)]](examples/atari/train_a2c_ale.py)
+- [A3C (Asynchronous Advantage Actor-Critic)](https://arxiv.org/abs/1602.01783)
+  - examples: [[atari reproduction]](examples/atari/reproduction/a3c) [[atari]](examples/atari/train_a3c_ale.py)
+- [ACER (Actor-Critic with Experience Replay)](https://arxiv.org/abs/1611.01224)
+  - examples: [[atari]](examples/atari/train_acer_ale.py)
+- [Categorical DQN](https://arxiv.org/abs/1707.06887)
+  - examples: [[atari]](examples/atari/train_categorical_dqn_ale.py) [[general gym]](examples/gym/train_categorical_dqn_gym.py)
+- [DQN (Deep Q-Network)](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) (including [Double DQN](https://arxiv.org/abs/1509.06461), [Persistent Advantage Learning (PAL)](https://arxiv.org/abs/1512.04860), Double PAL, [Dynamic Policy Programming (DPP)](http://www.jmlr.org/papers/volume13/azar12a/azar12a.pdf))
+  - examples: [[atari reproduction]](examples/atari/reproduction/dqn) [[atari]](examples/atari/train_dqn_ale.py) [[atari (batched)]](examples/atari/train_dqn_batch_ale.py) [[flickering atari]](examples/atari/train_drqn_ale.py) [[general gym]](examples/gym/train_dqn_gym.py)
+- [DDPG (Deep Deterministic Policy Gradients)](https://arxiv.org/abs/1509.02971) (including [SVG(0)](https://arxiv.org/abs/1510.09142))
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/ddpg)
+- [IQN (Implicit Quantile Networks)](https://arxiv.org/abs/1806.06923)
+  - examples: [[atari reproduction]](examples/atari/reproduction/iqn)
+- [PPO (Proximal Policy Optimization)](https://arxiv.org/abs/1707.06347)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/ppo) [[atari]](examples/atari/train_ppo_ale.py)
+- [Rainbow](https://arxiv.org/abs/1710.02298)
+  - examples: [[atari reproduction]](examples/atari/reproduction/rainbow) [[Slime volleyball]](examples/slimevolley/)
+- [REINFORCE](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)
+  - examples: [[general gym]](examples/gym/train_reinforce_gym.py)
+- [SAC (Soft Actor-Critic)](https://arxiv.org/abs/1812.05905)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/soft_actor_critic) [[Atlas walk]](examples/atlas/)
+- [TRPO (Trust Region Policy Optimization)](https://arxiv.org/abs/1502.05477) with [GAE (Generalized Advantage Estimation)](https://arxiv.org/abs/1506.02438)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/trpo)
+- [TD3 (Twin Delayed Deep Deterministic policy gradient algorithm)](https://arxiv.org/abs/1802.09477)
+  - examples: [[mujoco reproduction]](examples/mujoco/reproduction/td3)
+
+Following useful techniques have been also implemented in PFRL:
+- [NoisyNet](https://arxiv.org/abs/1706.10295)
+  - examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
+- [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)
+  - examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
+- [Dueling Network](https://arxiv.org/abs/1511.06581)
+  - examples: [[Rainbow]](examples/atari/reproduction/rainbow) [[DQN/DoubleDQN/PAL]](examples/atari/train_dqn_ale.py)
+- [Normalized Advantage Function](https://arxiv.org/abs/1603.00748)
+  - examples: [[DQN]](examples/gym/train_dqn_gym.py) (for continuous-action envs only)
+- [Deep Recurrent Q-Network](https://arxiv.org/abs/1507.06527)
+  - examples: [[DQN]](examples/atari/train_drqn_ale.py)
+
+
+## Environments
+
+Environments that support the subset of OpenAI Gym's interface (`reset` and `step` methods) can be used.
+
+## Contributing
+
+Any kind of contribution to PFRL would be highly appreciated! If you are interested in contributing to PFRL, please read [CONTRIBUTING.md](CONTRIBUTING.md).
+
+## License
+
+[MIT License](LICENSE).
+
+## Citations
+
+To cite PFRL in publications, please cite our [paper](https://www.jmlr.org/papers/v22/20-376.html) on ChainerRL, the library on which PFRL is based:
+
+```
+@article{JMLR:v22:20-376,
+  author  = {Yasuhiro Fujita and Prabhat Nagarajan and Toshiki Kataoka and Takahiro Ishikawa},
+  title   = {ChainerRL: A Deep Reinforcement Learning Library},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {77},
+  pages   = {1-14},
+  url     = {http://jmlr.org/papers/v22/20-376.html}
+}
+```
+
+%prep
+%autosetup -n pfrl-0.3.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-pfrl -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.0-1
+- Package Spec generated