%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.aliyun.com/pypi/web/packages/de/92/23746242ff9a626157ef24b26913fcfb94011b98527758573c7c2f59d41d/pogema-1.1.6.tar.gz BuildArch: noarch Requires: python3-gym Requires: python3-numpy Requires: python3-pydantic %description [![Pogema logo](https://raw.githubusercontent.com/Tviskaron/pogema-pics/main/pogema-logo.svg)](https://github.com/AIRI-Institute/pogema) **Partially-Observable Grid Environment for Multiple Agents** [![CodeFactor](https://www.codefactor.io/repository/github/tviskaron/pogema/badge)](https://www.codefactor.io/repository/github/tviskaron/pogema) [![Downloads](https://pepy.tech/badge/pogema)](https://pepy.tech/project/pogema) [![CI](https://github.com/AIRI-Institute/pogema/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/AIRI-Institute/pogema/actions/workflows/CI.yml) [![CodeQL](https://github.com/AIRI-Institute/pogema/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/AIRI-Institute/pogema/actions/workflows/codeql-analysis.yml) 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))]) ``` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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 [![Pogema logo](https://raw.githubusercontent.com/Tviskaron/pogema-pics/main/pogema-logo.svg)](https://github.com/AIRI-Institute/pogema) **Partially-Observable Grid Environment for Multiple Agents** [![CodeFactor](https://www.codefactor.io/repository/github/tviskaron/pogema/badge)](https://www.codefactor.io/repository/github/tviskaron/pogema) [![Downloads](https://pepy.tech/badge/pogema)](https://pepy.tech/project/pogema) [![CI](https://github.com/AIRI-Institute/pogema/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/AIRI-Institute/pogema/actions/workflows/CI.yml) [![CodeQL](https://github.com/AIRI-Institute/pogema/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/AIRI-Institute/pogema/actions/workflows/codeql-analysis.yml) 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))]) ``` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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 [![Pogema logo](https://raw.githubusercontent.com/Tviskaron/pogema-pics/main/pogema-logo.svg)](https://github.com/AIRI-Institute/pogema) **Partially-Observable Grid Environment for Multiple Agents** [![CodeFactor](https://www.codefactor.io/repository/github/tviskaron/pogema/badge)](https://www.codefactor.io/repository/github/tviskaron/pogema) [![Downloads](https://pepy.tech/badge/pogema)](https://pepy.tech/project/pogema) [![CI](https://github.com/AIRI-Institute/pogema/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/AIRI-Institute/pogema/actions/workflows/CI.yml) [![CodeQL](https://github.com/AIRI-Institute/pogema/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/AIRI-Institute/pogema/actions/workflows/codeql-analysis.yml) 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))]) ``` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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 * Thu Jun 08 2023 Python_Bot - 1.1.6-1 - Package Spec generated