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diff --git a/python-pogema.spec b/python-pogema.spec new file mode 100644 index 0000000..8560646 --- /dev/null +++ b/python-pogema.spec @@ -0,0 +1,606 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pogema +Version: 1.1.6 +Release: 1 +Summary: Partially Observable Grid Environment for Multiple Agents +License: MIT +URL: https://github.com/AIRI-Institute/pogema +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/de/92/23746242ff9a626157ef24b26913fcfb94011b98527758573c7c2f59d41d/pogema-1.1.6.tar.gz +BuildArch: noarch + +Requires: python3-gym +Requires: python3-numpy +Requires: python3-pydantic + +%description + + +[](https://github.com/AIRI-Institute/pogema) + +**Partially-Observable Grid Environment for Multiple Agents** + +[](https://www.codefactor.io/repository/github/tviskaron/pogema) +[](https://pepy.tech/project/pogema) +[](https://github.com/AIRI-Institute/pogema/actions/workflows/CI.yml) +[](https://github.com/AIRI-Institute/pogema/actions/workflows/codeql-analysis.yml) + +</div> + +Partially observable multi-agent pathfinding (PO-MAPF) is a challenging problem which fundamentally differs from regular MAPF, in which a central controller is assumed to construct a joint plan for all agents before they start execution. PO-MAPF is intrisically decentralized and decision making (e.g. planning) here is interleaved with the execution. At each time step an agent receives a (local) observation of the environment and decides which action to take. The ultimate goal for the agents is to reach their goals while avoiding collisions with each other and the static obstacles. + +POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings. Currently the (somewhat) standard setting is supported: agents can move between the cardinally-adjacent cells of the grid, each action (move or wait) takes one time step. No information sharing between the agents is happening. + +POGEMA can generate random maps and start/goals locations for the agents. It also can take custom maps as the input. + +## Installation + +Just install from PyPI: + +```pip install pogema``` + +## Using Example + +```python +from pogema import pogema_v0, Hard8x8 +env = pogema_v0(grid_config=Hard8x8()) + +obs = env.reset() + +done = [False, ...] + +while not all(done): + # Use random policy to make actions + obs, reward, done, info = env.step([env.action_space.sample() for _ in range(len(obs))]) +``` + +[](https://colab.research.google.com/drive/19dSEGTQeM3oVJtVjpC162t1XApmv6APc?usp=sharing) + +## Environments + +| Config | agents density | num agents | horizon | +| ----------------- | ----- | ----- | ---- | +| Easy8x8 | 2.2% | 1 | 64 | +| Normal8x8 | 4.5% | 2 | 64 | +| Hard8x8 | 8.9% | 4 | 64 | +| ExtraHard8x8 | 17.8% | 8 | 64 | +| Easy16x16 | 2.2% | 4 | 128 | +| Normal16x16 | 4.5% | 8 | 128 | +| Hard16x16 | 8.9% | 16 | 128 | +| ExtraHard16x16 | 17.8% | 32 | 128 | +| Easy32x32 | 2.2% | 16 | 256 | +| Normal32x32 | 4.5% | 32 | 256 | +| Hard32x32 | 8.9% | 64 | 256 | +| ExtraHard32x32 | 17.8% | 128 | 256 | +| Easy64x64 | 2.2% | 64 | 512 | +| Normal64x64 | 4.5% | 128 | 512 | +| Hard64x64 | 8.9% | 256 | 512 | +| ExtraHard64x64 | 17.8% | 512 | 512 | + +## Baselines +The [baseline implementations](https://github.com/Tviskaron/pogema-baselines) are available as a separate repository. + +## Interfaces +Pogema provides integrations with a range of MARL frameworks: PettingZoo, PyMARL and SampleFactory. + +### PettingZoo + +```python +from pogema import pogema_v0, GridConfig + +# Create Pogema environment with PettingZoo interface +env = pogema_v0(GridConfig(integration="PettingZoo")) +``` + +### PyMARL + +```python +from pogema import pogema_v0, GridConfig + +env = pogema_v0(GridConfig(integration="PyMARL")) +``` + +### SampleFactory + +```python +from pogema import pogema_v0, GridConfig + +env = pogema_v0(GridConfig(integration="SampleFactory")) +``` + +### Classic Gym + +Pogema is fully capable for single-agent pathfinding tasks. + +```python +import gym +import pogema + +# This interface provides experience only for agent with id=0, +# other agents will take random actions. +env = gym.make("Pogema-v0") +``` + +Example of training [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) DQN to solve single-agent pathfinding tasks: [](https://colab.research.google.com/drive/1vPwTd0PnzpWrB-bCHqoLSVwU9G9Lgcmv?usp=sharing) + + + + +## Customization + +### Random maps +```python +from pogema import pogema_v0, GridConfig + +# Define random configuration +grid_config = GridConfig(num_agents=4, # number of agents + size=8, # size of the grid + density=0.4, # obstacle density + seed=1, # set to None for random + # obstacles, agents and targets + # positions at each reset + max_episode_steps=128, # horizon + obs_radius=3, # defines field of view + ) + +env = pogema_v0(grid_config=grid_config) +env.reset() +env.render() + +``` + +### Custom maps +```python +from pogema import pogema_v0, GridConfig + +grid = """ +.....#..... +.....#..... +........... +.....#..... +.....#..... +#.####..... +.....###.## +.....#..... +.....#..... +........... +.....#..... +""" + +# Define new configuration with 8 randomly placed agents +grid_config = GridConfig(map=grid, num_agents=8) + +# Create custom Pogema environment +env = pogema_v0(grid_config=grid_config) +``` + + + + +## Citation +If you use this repository in your research or wish to cite it, please make a reference to our paper: +``` +@misc{https://doi.org/10.48550/arxiv.2206.10944, + doi = {10.48550/ARXIV.2206.10944}, + url = {https://arxiv.org/abs/2206.10944}, + author = {Skrynnik, Alexey and Andreychuk, Anton and Yakovlev, Konstantin and Panov, Aleksandr I.}, + keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Multiagent Systems (cs.MA), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {POGEMA: Partially Observable Grid Environment for Multiple Agents}, + publisher = {arXiv}, + year = {2022}, + copyright = {arXiv.org perpetual, non-exclusive license} +} +``` + + +%package -n python3-pogema +Summary: Partially Observable Grid Environment for Multiple Agents +Provides: python-pogema +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pogema + + +[](https://github.com/AIRI-Institute/pogema) + +**Partially-Observable Grid Environment for Multiple Agents** + +[](https://www.codefactor.io/repository/github/tviskaron/pogema) +[](https://pepy.tech/project/pogema) +[](https://github.com/AIRI-Institute/pogema/actions/workflows/CI.yml) +[](https://github.com/AIRI-Institute/pogema/actions/workflows/codeql-analysis.yml) + +</div> + +Partially observable multi-agent pathfinding (PO-MAPF) is a challenging problem which fundamentally differs from regular MAPF, in which a central controller is assumed to construct a joint plan for all agents before they start execution. PO-MAPF is intrisically decentralized and decision making (e.g. planning) here is interleaved with the execution. At each time step an agent receives a (local) observation of the environment and decides which action to take. The ultimate goal for the agents is to reach their goals while avoiding collisions with each other and the static obstacles. + +POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings. Currently the (somewhat) standard setting is supported: agents can move between the cardinally-adjacent cells of the grid, each action (move or wait) takes one time step. No information sharing between the agents is happening. + +POGEMA can generate random maps and start/goals locations for the agents. It also can take custom maps as the input. + +## Installation + +Just install from PyPI: + +```pip install pogema``` + +## Using Example + +```python +from pogema import pogema_v0, Hard8x8 +env = pogema_v0(grid_config=Hard8x8()) + +obs = env.reset() + +done = [False, ...] + +while not all(done): + # Use random policy to make actions + obs, reward, done, info = env.step([env.action_space.sample() for _ in range(len(obs))]) +``` + +[](https://colab.research.google.com/drive/19dSEGTQeM3oVJtVjpC162t1XApmv6APc?usp=sharing) + +## Environments + +| Config | agents density | num agents | horizon | +| ----------------- | ----- | ----- | ---- | +| Easy8x8 | 2.2% | 1 | 64 | +| Normal8x8 | 4.5% | 2 | 64 | +| Hard8x8 | 8.9% | 4 | 64 | +| ExtraHard8x8 | 17.8% | 8 | 64 | +| Easy16x16 | 2.2% | 4 | 128 | +| Normal16x16 | 4.5% | 8 | 128 | +| Hard16x16 | 8.9% | 16 | 128 | +| ExtraHard16x16 | 17.8% | 32 | 128 | +| Easy32x32 | 2.2% | 16 | 256 | +| Normal32x32 | 4.5% | 32 | 256 | +| Hard32x32 | 8.9% | 64 | 256 | +| ExtraHard32x32 | 17.8% | 128 | 256 | +| Easy64x64 | 2.2% | 64 | 512 | +| Normal64x64 | 4.5% | 128 | 512 | +| Hard64x64 | 8.9% | 256 | 512 | +| ExtraHard64x64 | 17.8% | 512 | 512 | + +## Baselines +The [baseline implementations](https://github.com/Tviskaron/pogema-baselines) are available as a separate repository. + +## Interfaces +Pogema provides integrations with a range of MARL frameworks: PettingZoo, PyMARL and SampleFactory. + +### PettingZoo + +```python +from pogema import pogema_v0, GridConfig + +# Create Pogema environment with PettingZoo interface +env = pogema_v0(GridConfig(integration="PettingZoo")) +``` + +### PyMARL + +```python +from pogema import pogema_v0, GridConfig + +env = pogema_v0(GridConfig(integration="PyMARL")) +``` + +### SampleFactory + +```python +from pogema import pogema_v0, GridConfig + +env = pogema_v0(GridConfig(integration="SampleFactory")) +``` + +### Classic Gym + +Pogema is fully capable for single-agent pathfinding tasks. + +```python +import gym +import pogema + +# This interface provides experience only for agent with id=0, +# other agents will take random actions. +env = gym.make("Pogema-v0") +``` + +Example of training [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) DQN to solve single-agent pathfinding tasks: [](https://colab.research.google.com/drive/1vPwTd0PnzpWrB-bCHqoLSVwU9G9Lgcmv?usp=sharing) + + + + +## Customization + +### Random maps +```python +from pogema import pogema_v0, GridConfig + +# Define random configuration +grid_config = GridConfig(num_agents=4, # number of agents + size=8, # size of the grid + density=0.4, # obstacle density + seed=1, # set to None for random + # obstacles, agents and targets + # positions at each reset + max_episode_steps=128, # horizon + obs_radius=3, # defines field of view + ) + +env = pogema_v0(grid_config=grid_config) +env.reset() +env.render() + +``` + +### Custom maps +```python +from pogema import pogema_v0, GridConfig + +grid = """ +.....#..... +.....#..... +........... +.....#..... +.....#..... +#.####..... +.....###.## +.....#..... +.....#..... +........... +.....#..... +""" + +# Define new configuration with 8 randomly placed agents +grid_config = GridConfig(map=grid, num_agents=8) + +# Create custom Pogema environment +env = pogema_v0(grid_config=grid_config) +``` + + + + +## Citation +If you use this repository in your research or wish to cite it, please make a reference to our paper: +``` +@misc{https://doi.org/10.48550/arxiv.2206.10944, + doi = {10.48550/ARXIV.2206.10944}, + url = {https://arxiv.org/abs/2206.10944}, + author = {Skrynnik, Alexey and Andreychuk, Anton and Yakovlev, Konstantin and Panov, Aleksandr I.}, + keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Multiagent Systems (cs.MA), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {POGEMA: Partially Observable Grid Environment for Multiple Agents}, + publisher = {arXiv}, + year = {2022}, + copyright = {arXiv.org perpetual, non-exclusive license} +} +``` + + +%package help +Summary: Development documents and examples for pogema +Provides: python3-pogema-doc +%description help + + +[](https://github.com/AIRI-Institute/pogema) + +**Partially-Observable Grid Environment for Multiple Agents** + +[](https://www.codefactor.io/repository/github/tviskaron/pogema) +[](https://pepy.tech/project/pogema) +[](https://github.com/AIRI-Institute/pogema/actions/workflows/CI.yml) +[](https://github.com/AIRI-Institute/pogema/actions/workflows/codeql-analysis.yml) + +</div> + +Partially observable multi-agent pathfinding (PO-MAPF) is a challenging problem which fundamentally differs from regular MAPF, in which a central controller is assumed to construct a joint plan for all agents before they start execution. PO-MAPF is intrisically decentralized and decision making (e.g. planning) here is interleaved with the execution. At each time step an agent receives a (local) observation of the environment and decides which action to take. The ultimate goal for the agents is to reach their goals while avoiding collisions with each other and the static obstacles. + +POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings. Currently the (somewhat) standard setting is supported: agents can move between the cardinally-adjacent cells of the grid, each action (move or wait) takes one time step. No information sharing between the agents is happening. + +POGEMA can generate random maps and start/goals locations for the agents. It also can take custom maps as the input. + +## Installation + +Just install from PyPI: + +```pip install pogema``` + +## Using Example + +```python +from pogema import pogema_v0, Hard8x8 +env = pogema_v0(grid_config=Hard8x8()) + +obs = env.reset() + +done = [False, ...] + +while not all(done): + # Use random policy to make actions + obs, reward, done, info = env.step([env.action_space.sample() for _ in range(len(obs))]) +``` + +[](https://colab.research.google.com/drive/19dSEGTQeM3oVJtVjpC162t1XApmv6APc?usp=sharing) + +## Environments + +| Config | agents density | num agents | horizon | +| ----------------- | ----- | ----- | ---- | +| Easy8x8 | 2.2% | 1 | 64 | +| Normal8x8 | 4.5% | 2 | 64 | +| Hard8x8 | 8.9% | 4 | 64 | +| ExtraHard8x8 | 17.8% | 8 | 64 | +| Easy16x16 | 2.2% | 4 | 128 | +| Normal16x16 | 4.5% | 8 | 128 | +| Hard16x16 | 8.9% | 16 | 128 | +| ExtraHard16x16 | 17.8% | 32 | 128 | +| Easy32x32 | 2.2% | 16 | 256 | +| Normal32x32 | 4.5% | 32 | 256 | +| Hard32x32 | 8.9% | 64 | 256 | +| ExtraHard32x32 | 17.8% | 128 | 256 | +| Easy64x64 | 2.2% | 64 | 512 | +| Normal64x64 | 4.5% | 128 | 512 | +| Hard64x64 | 8.9% | 256 | 512 | +| ExtraHard64x64 | 17.8% | 512 | 512 | + +## Baselines +The [baseline implementations](https://github.com/Tviskaron/pogema-baselines) are available as a separate repository. + +## Interfaces +Pogema provides integrations with a range of MARL frameworks: PettingZoo, PyMARL and SampleFactory. + +### PettingZoo + +```python +from pogema import pogema_v0, GridConfig + +# Create Pogema environment with PettingZoo interface +env = pogema_v0(GridConfig(integration="PettingZoo")) +``` + +### PyMARL + +```python +from pogema import pogema_v0, GridConfig + +env = pogema_v0(GridConfig(integration="PyMARL")) +``` + +### SampleFactory + +```python +from pogema import pogema_v0, GridConfig + +env = pogema_v0(GridConfig(integration="SampleFactory")) +``` + +### Classic Gym + +Pogema is fully capable for single-agent pathfinding tasks. + +```python +import gym +import pogema + +# This interface provides experience only for agent with id=0, +# other agents will take random actions. +env = gym.make("Pogema-v0") +``` + +Example of training [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) DQN to solve single-agent pathfinding tasks: [](https://colab.research.google.com/drive/1vPwTd0PnzpWrB-bCHqoLSVwU9G9Lgcmv?usp=sharing) + + + + +## Customization + +### Random maps +```python +from pogema import pogema_v0, GridConfig + +# Define random configuration +grid_config = GridConfig(num_agents=4, # number of agents + size=8, # size of the grid + density=0.4, # obstacle density + seed=1, # set to None for random + # obstacles, agents and targets + # positions at each reset + max_episode_steps=128, # horizon + obs_radius=3, # defines field of view + ) + +env = pogema_v0(grid_config=grid_config) +env.reset() +env.render() + +``` + +### Custom maps +```python +from pogema import pogema_v0, GridConfig + +grid = """ +.....#..... +.....#..... +........... +.....#..... +.....#..... +#.####..... +.....###.## +.....#..... +.....#..... +........... +.....#..... +""" + +# Define new configuration with 8 randomly placed agents +grid_config = GridConfig(map=grid, num_agents=8) + +# Create custom Pogema environment +env = pogema_v0(grid_config=grid_config) +``` + + + + +## Citation +If you use this repository in your research or wish to cite it, please make a reference to our paper: +``` +@misc{https://doi.org/10.48550/arxiv.2206.10944, + doi = {10.48550/ARXIV.2206.10944}, + url = {https://arxiv.org/abs/2206.10944}, + author = {Skrynnik, Alexey and Andreychuk, Anton and Yakovlev, Konstantin and Panov, Aleksandr I.}, + keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Multiagent Systems (cs.MA), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {POGEMA: Partially Observable Grid Environment for Multiple Agents}, + publisher = {arXiv}, + year = {2022}, + copyright = {arXiv.org perpetual, non-exclusive license} +} +``` + + +%prep +%autosetup -n pogema-1.1.6 + +%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-pogema -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.6-1 +- Package Spec generated |