%global _empty_manifest_terminate_build 0 Name: python-dopamine-rl Version: 4.0.6 Release: 1 Summary: Dopamine: A framework for flexible Reinforcement Learning research License: Apache 2.0 URL: https://github.com/google/dopamine Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ec/ec/ab07ca64802f209f7dc23a653c91015fb7459fba60866279684ded589725/dopamine_rl-4.0.6.tar.gz BuildArch: noarch Requires: python3-tensorflow Requires: python3-gin-config Requires: python3-absl-py Requires: python3-opencv-python Requires: python3-gym Requires: python3-flax Requires: python3-jax Requires: python3-jaxlib Requires: python3-Pillow Requires: python3-numpy Requires: python3-pygame Requires: python3-pandas Requires: python3-tf-slim Requires: python3-tensorflow-probability %description # Dopamine [Getting Started](#getting-started) | [Docs][docs] | [Baseline Results][baselines] | [Changelist](https://google.github.io/dopamine/docs/changelist)


Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). Our design principles are: * _Easy experimentation_: Make it easy for new users to run benchmark experiments. * _Flexible development_: Make it easy for new users to try out research ideas. * _Compact and reliable_: Provide implementations for a few, battle-tested algorithms. * _Reproducible_: Facilitate reproducibility in results. In particular, our setup follows the recommendations given by [Machado et al. (2018)][machado]. Dopamine supports the following agents, implemented with jax: * DQN ([Mnih et al., 2015][dqn]) * C51 ([Bellemare et al., 2017][c51]) * Rainbow ([Hessel et al., 2018][rainbow]) * IQN ([Dabney et al., 2018][iqn]) * SAC ([Haarnoja et al., 2018][sac]) For more information on the available agents, see the [docs](https://google.github.io/dopamine/docs). Many of these agents also have a tensorflow (legacy) implementation, though newly added agents are likely to be jax-only. This is not an official Google product. ## Getting Started We provide docker containers for using Dopamine. Instructions can be found [here](https://google.github.io/dopamine/docker/). Alternatively, Dopamine can be installed from source (preferred) or installed with pip. For either of these methods, continue reading at prerequisites. ### Prerequisites Dopamine supports Atari environments and Mujoco environments. Install the environments you intend to use before you install Dopamine: **Atari** 1. Install the atari roms following the instructions from [atari-py](https://github.com/openai/atari-py#roms). 2. `pip install ale-py` (we recommend using a [virtual environment](virtualenv)): 3. `unzip $ROM_DIR/ROMS.zip -d $ROM_DIR && ale-import-roms $ROM_DIR/ROMS` (replace $ROM_DIR with the directory you extracted the ROMs to). **Mujoco** 1. Install Mujoco and get a license [here](https://github.com/openai/mujoco-py#install-mujoco). 2. Run `pip install mujoco-py` (we recommend using a [virtual environment](virtualenv)). ### Installing from Source The most common way to use Dopamine is to install it from source and modify the source code directly: ``` git clone https://github.com/google/dopamine ``` After cloning, install dependencies: ``` pip install -r dopamine/requirements.txt ``` Dopamine supports tensorflow (legacy) and jax (actively maintained) agents. View the [Tensorflow documentation](https://www.tensorflow.org/install) for more information on installing tensorflow. Note: We recommend using a [virtual environment](virtualenv) when working with Dopamine. ### Installing with Pip Note: We strongly recommend installing from source for most users. Installing with pip is simple, but Dopamine is designed to be modified directly. We recommend installing from source for writing your own experiments. ``` pip install dopamine-rl ``` ### Running tests You can test whether the installation was successful by running the following from the dopamine root directory. ``` export PYTHONPATH=$PYTHONPATH:$PWD python -m tests.dopamine.atari_init_test ``` ## Next Steps View the [docs][docs] for more information on training agents. We supply [baselines][baselines] for each Dopamine agent. We also provide a set of [Colaboratory notebooks](https://github.com/google/dopamine/tree/master/dopamine/colab) which demonstrate how to use Dopamine. ## References [Bellemare et al., *The Arcade Learning Environment: An evaluation platform for general agents*. Journal of Artificial Intelligence Research, 2013.][ale] [Machado et al., *Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents*, Journal of Artificial Intelligence Research, 2018.][machado] [Hessel et al., *Rainbow: Combining Improvements in Deep Reinforcement Learning*. Proceedings of the AAAI Conference on Artificial Intelligence, 2018.][rainbow] [Mnih et al., *Human-level Control through Deep Reinforcement Learning*. Nature, 2015.][dqn] [Schaul et al., *Prioritized Experience Replay*. Proceedings of the International Conference on Learning Representations, 2016.][prioritized_replay] [Haarnoja et al., *Soft Actor-Critic Algorithms and Applications*, arXiv preprint arXiv:1812.05905, 2018.][sac] ## Giving credit If you use Dopamine in your work, we ask that you cite our [white paper][dopamine_paper]. Here is an example BibTeX entry: ``` @article{castro18dopamine, author = {Pablo Samuel Castro and Subhodeep Moitra and Carles Gelada and Saurabh Kumar and Marc G. Bellemare}, title = {Dopamine: {A} {R}esearch {F}ramework for {D}eep {R}einforcement {L}earning}, year = {2018}, url = {http://arxiv.org/abs/1812.06110}, archivePrefix = {arXiv} } ``` [docs]: https://google.github.io/dopamine/docs/ [baselines]: https://google.github.io/dopamine/baselines [machado]: https://jair.org/index.php/jair/article/view/11182 [ale]: https://jair.org/index.php/jair/article/view/10819 [dqn]: https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf [a3c]: http://proceedings.mlr.press/v48/mniha16.html [prioritized_replay]: https://arxiv.org/abs/1511.05952 [c51]: http://proceedings.mlr.press/v70/bellemare17a.html [rainbow]: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17204/16680 [iqn]: https://arxiv.org/abs/1806.06923 [sac]: https://arxiv.org/abs/1812.05905 [dopamine_paper]: https://arxiv.org/abs/1812.06110 [vitualenv]: https://docs.python.org/3/library/venv.html#creating-virtual-environments %package -n python3-dopamine-rl Summary: Dopamine: A framework for flexible Reinforcement Learning research Provides: python-dopamine-rl BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dopamine-rl # Dopamine [Getting Started](#getting-started) | [Docs][docs] | [Baseline Results][baselines] | [Changelist](https://google.github.io/dopamine/docs/changelist)


Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). Our design principles are: * _Easy experimentation_: Make it easy for new users to run benchmark experiments. * _Flexible development_: Make it easy for new users to try out research ideas. * _Compact and reliable_: Provide implementations for a few, battle-tested algorithms. * _Reproducible_: Facilitate reproducibility in results. In particular, our setup follows the recommendations given by [Machado et al. (2018)][machado]. Dopamine supports the following agents, implemented with jax: * DQN ([Mnih et al., 2015][dqn]) * C51 ([Bellemare et al., 2017][c51]) * Rainbow ([Hessel et al., 2018][rainbow]) * IQN ([Dabney et al., 2018][iqn]) * SAC ([Haarnoja et al., 2018][sac]) For more information on the available agents, see the [docs](https://google.github.io/dopamine/docs). Many of these agents also have a tensorflow (legacy) implementation, though newly added agents are likely to be jax-only. This is not an official Google product. ## Getting Started We provide docker containers for using Dopamine. Instructions can be found [here](https://google.github.io/dopamine/docker/). Alternatively, Dopamine can be installed from source (preferred) or installed with pip. For either of these methods, continue reading at prerequisites. ### Prerequisites Dopamine supports Atari environments and Mujoco environments. Install the environments you intend to use before you install Dopamine: **Atari** 1. Install the atari roms following the instructions from [atari-py](https://github.com/openai/atari-py#roms). 2. `pip install ale-py` (we recommend using a [virtual environment](virtualenv)): 3. `unzip $ROM_DIR/ROMS.zip -d $ROM_DIR && ale-import-roms $ROM_DIR/ROMS` (replace $ROM_DIR with the directory you extracted the ROMs to). **Mujoco** 1. Install Mujoco and get a license [here](https://github.com/openai/mujoco-py#install-mujoco). 2. Run `pip install mujoco-py` (we recommend using a [virtual environment](virtualenv)). ### Installing from Source The most common way to use Dopamine is to install it from source and modify the source code directly: ``` git clone https://github.com/google/dopamine ``` After cloning, install dependencies: ``` pip install -r dopamine/requirements.txt ``` Dopamine supports tensorflow (legacy) and jax (actively maintained) agents. View the [Tensorflow documentation](https://www.tensorflow.org/install) for more information on installing tensorflow. Note: We recommend using a [virtual environment](virtualenv) when working with Dopamine. ### Installing with Pip Note: We strongly recommend installing from source for most users. Installing with pip is simple, but Dopamine is designed to be modified directly. We recommend installing from source for writing your own experiments. ``` pip install dopamine-rl ``` ### Running tests You can test whether the installation was successful by running the following from the dopamine root directory. ``` export PYTHONPATH=$PYTHONPATH:$PWD python -m tests.dopamine.atari_init_test ``` ## Next Steps View the [docs][docs] for more information on training agents. We supply [baselines][baselines] for each Dopamine agent. We also provide a set of [Colaboratory notebooks](https://github.com/google/dopamine/tree/master/dopamine/colab) which demonstrate how to use Dopamine. ## References [Bellemare et al., *The Arcade Learning Environment: An evaluation platform for general agents*. Journal of Artificial Intelligence Research, 2013.][ale] [Machado et al., *Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents*, Journal of Artificial Intelligence Research, 2018.][machado] [Hessel et al., *Rainbow: Combining Improvements in Deep Reinforcement Learning*. Proceedings of the AAAI Conference on Artificial Intelligence, 2018.][rainbow] [Mnih et al., *Human-level Control through Deep Reinforcement Learning*. Nature, 2015.][dqn] [Schaul et al., *Prioritized Experience Replay*. Proceedings of the International Conference on Learning Representations, 2016.][prioritized_replay] [Haarnoja et al., *Soft Actor-Critic Algorithms and Applications*, arXiv preprint arXiv:1812.05905, 2018.][sac] ## Giving credit If you use Dopamine in your work, we ask that you cite our [white paper][dopamine_paper]. Here is an example BibTeX entry: ``` @article{castro18dopamine, author = {Pablo Samuel Castro and Subhodeep Moitra and Carles Gelada and Saurabh Kumar and Marc G. Bellemare}, title = {Dopamine: {A} {R}esearch {F}ramework for {D}eep {R}einforcement {L}earning}, year = {2018}, url = {http://arxiv.org/abs/1812.06110}, archivePrefix = {arXiv} } ``` [docs]: https://google.github.io/dopamine/docs/ [baselines]: https://google.github.io/dopamine/baselines [machado]: https://jair.org/index.php/jair/article/view/11182 [ale]: https://jair.org/index.php/jair/article/view/10819 [dqn]: https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf [a3c]: http://proceedings.mlr.press/v48/mniha16.html [prioritized_replay]: https://arxiv.org/abs/1511.05952 [c51]: http://proceedings.mlr.press/v70/bellemare17a.html [rainbow]: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17204/16680 [iqn]: https://arxiv.org/abs/1806.06923 [sac]: https://arxiv.org/abs/1812.05905 [dopamine_paper]: https://arxiv.org/abs/1812.06110 [vitualenv]: https://docs.python.org/3/library/venv.html#creating-virtual-environments %package help Summary: Development documents and examples for dopamine-rl Provides: python3-dopamine-rl-doc %description help # Dopamine [Getting Started](#getting-started) | [Docs][docs] | [Baseline Results][baselines] | [Changelist](https://google.github.io/dopamine/docs/changelist)


Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). Our design principles are: * _Easy experimentation_: Make it easy for new users to run benchmark experiments. * _Flexible development_: Make it easy for new users to try out research ideas. * _Compact and reliable_: Provide implementations for a few, battle-tested algorithms. * _Reproducible_: Facilitate reproducibility in results. In particular, our setup follows the recommendations given by [Machado et al. (2018)][machado]. Dopamine supports the following agents, implemented with jax: * DQN ([Mnih et al., 2015][dqn]) * C51 ([Bellemare et al., 2017][c51]) * Rainbow ([Hessel et al., 2018][rainbow]) * IQN ([Dabney et al., 2018][iqn]) * SAC ([Haarnoja et al., 2018][sac]) For more information on the available agents, see the [docs](https://google.github.io/dopamine/docs). Many of these agents also have a tensorflow (legacy) implementation, though newly added agents are likely to be jax-only. This is not an official Google product. ## Getting Started We provide docker containers for using Dopamine. Instructions can be found [here](https://google.github.io/dopamine/docker/). Alternatively, Dopamine can be installed from source (preferred) or installed with pip. For either of these methods, continue reading at prerequisites. ### Prerequisites Dopamine supports Atari environments and Mujoco environments. Install the environments you intend to use before you install Dopamine: **Atari** 1. Install the atari roms following the instructions from [atari-py](https://github.com/openai/atari-py#roms). 2. `pip install ale-py` (we recommend using a [virtual environment](virtualenv)): 3. `unzip $ROM_DIR/ROMS.zip -d $ROM_DIR && ale-import-roms $ROM_DIR/ROMS` (replace $ROM_DIR with the directory you extracted the ROMs to). **Mujoco** 1. Install Mujoco and get a license [here](https://github.com/openai/mujoco-py#install-mujoco). 2. Run `pip install mujoco-py` (we recommend using a [virtual environment](virtualenv)). ### Installing from Source The most common way to use Dopamine is to install it from source and modify the source code directly: ``` git clone https://github.com/google/dopamine ``` After cloning, install dependencies: ``` pip install -r dopamine/requirements.txt ``` Dopamine supports tensorflow (legacy) and jax (actively maintained) agents. View the [Tensorflow documentation](https://www.tensorflow.org/install) for more information on installing tensorflow. Note: We recommend using a [virtual environment](virtualenv) when working with Dopamine. ### Installing with Pip Note: We strongly recommend installing from source for most users. Installing with pip is simple, but Dopamine is designed to be modified directly. We recommend installing from source for writing your own experiments. ``` pip install dopamine-rl ``` ### Running tests You can test whether the installation was successful by running the following from the dopamine root directory. ``` export PYTHONPATH=$PYTHONPATH:$PWD python -m tests.dopamine.atari_init_test ``` ## Next Steps View the [docs][docs] for more information on training agents. We supply [baselines][baselines] for each Dopamine agent. We also provide a set of [Colaboratory notebooks](https://github.com/google/dopamine/tree/master/dopamine/colab) which demonstrate how to use Dopamine. ## References [Bellemare et al., *The Arcade Learning Environment: An evaluation platform for general agents*. Journal of Artificial Intelligence Research, 2013.][ale] [Machado et al., *Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents*, Journal of Artificial Intelligence Research, 2018.][machado] [Hessel et al., *Rainbow: Combining Improvements in Deep Reinforcement Learning*. Proceedings of the AAAI Conference on Artificial Intelligence, 2018.][rainbow] [Mnih et al., *Human-level Control through Deep Reinforcement Learning*. Nature, 2015.][dqn] [Schaul et al., *Prioritized Experience Replay*. Proceedings of the International Conference on Learning Representations, 2016.][prioritized_replay] [Haarnoja et al., *Soft Actor-Critic Algorithms and Applications*, arXiv preprint arXiv:1812.05905, 2018.][sac] ## Giving credit If you use Dopamine in your work, we ask that you cite our [white paper][dopamine_paper]. Here is an example BibTeX entry: ``` @article{castro18dopamine, author = {Pablo Samuel Castro and Subhodeep Moitra and Carles Gelada and Saurabh Kumar and Marc G. Bellemare}, title = {Dopamine: {A} {R}esearch {F}ramework for {D}eep {R}einforcement {L}earning}, year = {2018}, url = {http://arxiv.org/abs/1812.06110}, archivePrefix = {arXiv} } ``` [docs]: https://google.github.io/dopamine/docs/ [baselines]: https://google.github.io/dopamine/baselines [machado]: https://jair.org/index.php/jair/article/view/11182 [ale]: https://jair.org/index.php/jair/article/view/10819 [dqn]: https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf [a3c]: http://proceedings.mlr.press/v48/mniha16.html [prioritized_replay]: https://arxiv.org/abs/1511.05952 [c51]: http://proceedings.mlr.press/v70/bellemare17a.html [rainbow]: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17204/16680 [iqn]: https://arxiv.org/abs/1806.06923 [sac]: https://arxiv.org/abs/1812.05905 [dopamine_paper]: https://arxiv.org/abs/1812.06110 [vitualenv]: https://docs.python.org/3/library/venv.html#creating-virtual-environments %prep %autosetup -n dopamine-rl-4.0.6 %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-dopamine-rl -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Apr 21 2023 Python_Bot - 4.0.6-1 - Package Spec generated