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@@ -0,0 +1 @@ +/pfrl-0.3.0.tar.gz diff --git a/python-pfrl.spec b/python-pfrl.spec new file mode 100644 index 0000000..393a03d --- /dev/null +++ b/python-pfrl.spec @@ -0,0 +1,450 @@ +%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 +[](http://pfrl.readthedocs.io/en/latest/?badge=latest) +[](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). + + + + + + + +## 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 +[](http://pfrl.readthedocs.io/en/latest/?badge=latest) +[](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). + + + + + + + +## 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 +[](http://pfrl.readthedocs.io/en/latest/?badge=latest) +[](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). + + + + + + + +## 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 @@ -0,0 +1 @@ +548b05e50b6c853edb8af417ee9d8ae9 pfrl-0.3.0.tar.gz |