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@@ -0,0 +1 @@ +/gym_minigrid-1.2.2.tar.gz diff --git a/python-gym-minigrid.spec b/python-gym-minigrid.spec new file mode 100644 index 0000000..1d7e3ba --- /dev/null +++ b/python-gym-minigrid.spec @@ -0,0 +1,349 @@ +%global _empty_manifest_terminate_build 0 +Name: python-gym-minigrid +Version: 1.2.2 +Release: 1 +Summary: Minimalistic gridworld reinforcement learning environments +License: Apache +URL: https://github.com/Farama-Foundation/gym-minigrid +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/68/23/501d0433991f580c8bf66fb15fb6ad57d87a152d1e8e0ebec8c383c0db38/gym_minigrid-1.2.2.tar.gz +BuildArch: noarch + +Requires: python3-gym +Requires: python3-numpy +Requires: python3-matplotlib +Requires: python3-pytest + +%description +# MiniGrid (formerly gym-minigrid) + +[](https://pre-commit.com/) +[](https://github.com/psf/black) + +There are other gridworld Gym environments out there, but this one is +designed to be particularly simple, lightweight and fast. The code has very few +dependencies, making it less likely to break or fail to install. It loads no +external sprites/textures, and it can run at up to 5000 FPS on a Core i7 +laptop, which means you can run your experiments faster. A known-working RL +implementation can be found [in this repository](https://github.com/lcswillems/torch-rl). + +Requirements: +- Python 3.7 to 3.10 +- OpenAI Gym v0.26 +- NumPy 1.18+ +- Matplotlib (optional, only needed for display) - 3.0+ + +Please use this bibtex if you want to cite this repository in your publications: + +``` +@misc{gym_minigrid, + author = {Chevalier-Boisvert, Maxime and Willems, Lucas and Pal, Suman}, + title = {Minimalistic Gridworld Environment for OpenAI Gym}, + year = {2018}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/maximecb/gym-minigrid}}, +} +``` + +List of publications & submissions using MiniGrid or BabyAI (please open a pull request to add missing entries): +- [History Compression via Language Models in Reinforcement Learning.](https://proceedings.mlr.press/v162/paischer22a.html) (Johannes Kepler University Linz, PMLR 2022) +- [Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity](https://arxiv.org/abs/2202.02886) (Arizona State University, ICML 2022) +- [How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation](https://proceedings.mlr.press/v162/mavor-parker22a.html) (University College London, Boston University, ICML 2022) +- [In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications](https://openreview.net/pdf?id=rUwm9wCjURV) (Imperial College London, ICLR 2022) +- [Interesting Object, Curious Agent: Learning Task-Agnostic Exploration](https://arxiv.org/abs/2111.13119) (Meta AI Research, NeurIPS 2021) +- [Safe Policy Optimization with Local Generalized Linear Function Approximations](https://arxiv.org/abs/2111.04894) (IBM Research, Tsinghua University, NeurIPS 2021) +- [A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning](https://arxiv.org/abs/2106.02097) (Mila, McGill University, NeurIPS 2021) +- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](http://www.ifaamas.org/Proceedings/aamas2021/pdfs/p1118.pdf) (Tufts University, SIFT, AAMAS 2021) +- [Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning](https://arxiv.org/abs/2102.04220) (UCL, AAMAS 2021) +- [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://openreview.net/forum?id=MtEE0CktZht) (Texas A&M University, Kuai Inc., ICLR 2021) +- [Adversarially Guided Actor-Critic](https://openreview.net/forum?id=_mQp5cr_iNy) (INRIA, Google Brain, ICLR 2021) +- [Information-theoretic Task Selection for Meta-Reinforcement Learning](https://papers.nips.cc/paper/2020/file/ec3183a7f107d1b8dbb90cb3c01ea7d5-Paper.pdf) (University of Leeds, NeurIPS 2020) +- [BeBold: Exploration Beyond the Boundary of Explored Regions](https://arxiv.org/pdf/2012.08621.pdf) (UCB, December 2020) +- [Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems](https://arxiv.org/abs/2010.08843) (McGill, October 2020) +- [Prioritized Level Replay](https://arxiv.org/pdf/2010.03934.pdf) (FAIR, October 2020) +- [AllenAct: A Framework for Embodied AI Research](https://arxiv.org/pdf/2008.12760.pdf) (Allen Institute for AI, August 2020) +- [Learning with AMIGO: Adversarially Motivated Intrinsic Goals](https://arxiv.org/pdf/2006.12122.pdf) (MIT, FAIR, ICLR 2021) +- [RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments](https://openreview.net/forum?id=rkg-TJBFPB) (FAIR, ICLR 2020) +- [Learning to Request Guidance in Emergent Communication](https://arxiv.org/pdf/1912.05525.pdf) (University of Amsterdam, Dec 2019) +- [Working Memory Graphs](https://arxiv.org/abs/1911.07141) (MSR, Nov 2019) +- [Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning](https://arxiv.org/pdf/1910.04040.pdf) (Oct 2019, University of Antwerp) +- [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck](https://arxiv.org/abs/1910.12911) (MSR, NeurIPS, Oct 2019) +- [Recurrent Independent Mechanisms](https://arxiv.org/pdf/1909.10893.pdf) (Mila, Sept 2019) +- [Learning Effective Subgoals with Multi-Task Hierarchical Reinforcement Learning](http://surl.tirl.info/proceedings/SURL-2019_paper_10.pdf) (Tsinghua University, August 2019) +- [Mastering emergent language: learning to guide in simulated navigation](https://arxiv.org/abs/1908.05135) (University of Amsterdam, Aug 2019) +- [Transfer Learning by Modeling a Distribution over Policies](https://arxiv.org/abs/1906.03574) (Mila, June 2019) +- [Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives](https://arxiv.org/abs/1906.10667) (Mila, June 2019) +- [Learning distant cause and effect using only local and immediate credit assignment](https://arxiv.org/abs/1905.11589) (Incubator 491, May 2019) +- [Practical Open-Loop Optimistic Planning](https://arxiv.org/abs/1904.04700) (INRIA, April 2019) +- [Learning World Graphs to Accelerate Hierarchical Reinforcement Learning](https://arxiv.org/abs/1907.00664) (Salesforce Research, 2019) +- [Variational State Encoding as Intrinsic Motivation in Reinforcement Learning](https://mila.quebec/wp-content/uploads/2019/05/WebPage.pdf) (Mila, TARL 2019) +- [Unsupervised Discovery of Decision States Through Intrinsic Control](https://tarl2019.github.io/assets/papers/modhe2019unsupervised.pdf) (Georgia Tech, TARL 2019) +- [Modeling the Long Term Future in Model-Based Reinforcement Learning](https://openreview.net/forum?id=SkgQBn0cF7) (Mila, ICLR 2019) +- [Unifying Ensemble Methods for Q-learning via Social Choice Theory](https://arxiv.org/pdf/1902.10646.pdf) (Max Planck Institute, Feb 2019) +- [Planning Beyond The Sensing Horizon Using a Learned Context](https://personalrobotics.cs.washington.edu/workshops/mlmp2018/assets/docs/18_CameraReadySubmission.pdf) (MLMP@IROS, 2018) +- [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882) (UC Berkeley, Nov 2018) +- [On the Complexity of Exploration in Goal-Driven Navigation](https://arxiv.org/abs/1811.06889) (CMU, NeurIPS, Nov 2018) +- [Transfer and Exploration via the Information Bottleneck](https://openreview.net/forum?id=rJg8yhAqKm) (Mila, Nov 2018) +- [Creating safer reward functions for reinforcement learning agents in the gridworld](https://gupea.ub.gu.se/bitstream/2077/62445/1/gupea_2077_62445_1.pdf) (University of Gothenburg, 2018) +- [BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop](https://arxiv.org/abs/1810.08272) (Mila, ICLR, Oct 2018) + +This environment has been built as part of work done at [Mila](https://mila.quebec). The Dynamic obstacles environment has been added as part of work done at [IAS in TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/) and the University of Genoa for mobile robot navigation with dynamic obstacles. + +## Installation + +There is now a [pip package](https://pypi.org/project/gym-minigrid/) available, which is updated periodically: + +``` +pip3 install gym-minigrid +``` + +Alternatively, to get the latest version of MiniGrid, you can clone this repository and install the dependencies with `pip3`: + +``` +git clone https://github.com/maximecb/gym-minigrid.git +cd gym-minigrid +pip3 install -e . +``` + + + +%package -n python3-gym-minigrid +Summary: Minimalistic gridworld reinforcement learning environments +Provides: python-gym-minigrid +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-gym-minigrid +# MiniGrid (formerly gym-minigrid) + +[](https://pre-commit.com/) +[](https://github.com/psf/black) + +There are other gridworld Gym environments out there, but this one is +designed to be particularly simple, lightweight and fast. The code has very few +dependencies, making it less likely to break or fail to install. It loads no +external sprites/textures, and it can run at up to 5000 FPS on a Core i7 +laptop, which means you can run your experiments faster. A known-working RL +implementation can be found [in this repository](https://github.com/lcswillems/torch-rl). + +Requirements: +- Python 3.7 to 3.10 +- OpenAI Gym v0.26 +- NumPy 1.18+ +- Matplotlib (optional, only needed for display) - 3.0+ + +Please use this bibtex if you want to cite this repository in your publications: + +``` +@misc{gym_minigrid, + author = {Chevalier-Boisvert, Maxime and Willems, Lucas and Pal, Suman}, + title = {Minimalistic Gridworld Environment for OpenAI Gym}, + year = {2018}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/maximecb/gym-minigrid}}, +} +``` + +List of publications & submissions using MiniGrid or BabyAI (please open a pull request to add missing entries): +- [History Compression via Language Models in Reinforcement Learning.](https://proceedings.mlr.press/v162/paischer22a.html) (Johannes Kepler University Linz, PMLR 2022) +- [Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity](https://arxiv.org/abs/2202.02886) (Arizona State University, ICML 2022) +- [How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation](https://proceedings.mlr.press/v162/mavor-parker22a.html) (University College London, Boston University, ICML 2022) +- [In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications](https://openreview.net/pdf?id=rUwm9wCjURV) (Imperial College London, ICLR 2022) +- [Interesting Object, Curious Agent: Learning Task-Agnostic Exploration](https://arxiv.org/abs/2111.13119) (Meta AI Research, NeurIPS 2021) +- [Safe Policy Optimization with Local Generalized Linear Function Approximations](https://arxiv.org/abs/2111.04894) (IBM Research, Tsinghua University, NeurIPS 2021) +- [A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning](https://arxiv.org/abs/2106.02097) (Mila, McGill University, NeurIPS 2021) +- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](http://www.ifaamas.org/Proceedings/aamas2021/pdfs/p1118.pdf) (Tufts University, SIFT, AAMAS 2021) +- [Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning](https://arxiv.org/abs/2102.04220) (UCL, AAMAS 2021) +- [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://openreview.net/forum?id=MtEE0CktZht) (Texas A&M University, Kuai Inc., ICLR 2021) +- [Adversarially Guided Actor-Critic](https://openreview.net/forum?id=_mQp5cr_iNy) (INRIA, Google Brain, ICLR 2021) +- [Information-theoretic Task Selection for Meta-Reinforcement Learning](https://papers.nips.cc/paper/2020/file/ec3183a7f107d1b8dbb90cb3c01ea7d5-Paper.pdf) (University of Leeds, NeurIPS 2020) +- [BeBold: Exploration Beyond the Boundary of Explored Regions](https://arxiv.org/pdf/2012.08621.pdf) (UCB, December 2020) +- [Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems](https://arxiv.org/abs/2010.08843) (McGill, October 2020) +- [Prioritized Level Replay](https://arxiv.org/pdf/2010.03934.pdf) (FAIR, October 2020) +- [AllenAct: A Framework for Embodied AI Research](https://arxiv.org/pdf/2008.12760.pdf) (Allen Institute for AI, August 2020) +- [Learning with AMIGO: Adversarially Motivated Intrinsic Goals](https://arxiv.org/pdf/2006.12122.pdf) (MIT, FAIR, ICLR 2021) +- [RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments](https://openreview.net/forum?id=rkg-TJBFPB) (FAIR, ICLR 2020) +- [Learning to Request Guidance in Emergent Communication](https://arxiv.org/pdf/1912.05525.pdf) (University of Amsterdam, Dec 2019) +- [Working Memory Graphs](https://arxiv.org/abs/1911.07141) (MSR, Nov 2019) +- [Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning](https://arxiv.org/pdf/1910.04040.pdf) (Oct 2019, University of Antwerp) +- [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck](https://arxiv.org/abs/1910.12911) (MSR, NeurIPS, Oct 2019) +- [Recurrent Independent Mechanisms](https://arxiv.org/pdf/1909.10893.pdf) (Mila, Sept 2019) +- [Learning Effective Subgoals with Multi-Task Hierarchical Reinforcement Learning](http://surl.tirl.info/proceedings/SURL-2019_paper_10.pdf) (Tsinghua University, August 2019) +- [Mastering emergent language: learning to guide in simulated navigation](https://arxiv.org/abs/1908.05135) (University of Amsterdam, Aug 2019) +- [Transfer Learning by Modeling a Distribution over Policies](https://arxiv.org/abs/1906.03574) (Mila, June 2019) +- [Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives](https://arxiv.org/abs/1906.10667) (Mila, June 2019) +- [Learning distant cause and effect using only local and immediate credit assignment](https://arxiv.org/abs/1905.11589) (Incubator 491, May 2019) +- [Practical Open-Loop Optimistic Planning](https://arxiv.org/abs/1904.04700) (INRIA, April 2019) +- [Learning World Graphs to Accelerate Hierarchical Reinforcement Learning](https://arxiv.org/abs/1907.00664) (Salesforce Research, 2019) +- [Variational State Encoding as Intrinsic Motivation in Reinforcement Learning](https://mila.quebec/wp-content/uploads/2019/05/WebPage.pdf) (Mila, TARL 2019) +- [Unsupervised Discovery of Decision States Through Intrinsic Control](https://tarl2019.github.io/assets/papers/modhe2019unsupervised.pdf) (Georgia Tech, TARL 2019) +- [Modeling the Long Term Future in Model-Based Reinforcement Learning](https://openreview.net/forum?id=SkgQBn0cF7) (Mila, ICLR 2019) +- [Unifying Ensemble Methods for Q-learning via Social Choice Theory](https://arxiv.org/pdf/1902.10646.pdf) (Max Planck Institute, Feb 2019) +- [Planning Beyond The Sensing Horizon Using a Learned Context](https://personalrobotics.cs.washington.edu/workshops/mlmp2018/assets/docs/18_CameraReadySubmission.pdf) (MLMP@IROS, 2018) +- [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882) (UC Berkeley, Nov 2018) +- [On the Complexity of Exploration in Goal-Driven Navigation](https://arxiv.org/abs/1811.06889) (CMU, NeurIPS, Nov 2018) +- [Transfer and Exploration via the Information Bottleneck](https://openreview.net/forum?id=rJg8yhAqKm) (Mila, Nov 2018) +- [Creating safer reward functions for reinforcement learning agents in the gridworld](https://gupea.ub.gu.se/bitstream/2077/62445/1/gupea_2077_62445_1.pdf) (University of Gothenburg, 2018) +- [BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop](https://arxiv.org/abs/1810.08272) (Mila, ICLR, Oct 2018) + +This environment has been built as part of work done at [Mila](https://mila.quebec). The Dynamic obstacles environment has been added as part of work done at [IAS in TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/) and the University of Genoa for mobile robot navigation with dynamic obstacles. + +## Installation + +There is now a [pip package](https://pypi.org/project/gym-minigrid/) available, which is updated periodically: + +``` +pip3 install gym-minigrid +``` + +Alternatively, to get the latest version of MiniGrid, you can clone this repository and install the dependencies with `pip3`: + +``` +git clone https://github.com/maximecb/gym-minigrid.git +cd gym-minigrid +pip3 install -e . +``` + + + +%package help +Summary: Development documents and examples for gym-minigrid +Provides: python3-gym-minigrid-doc +%description help +# MiniGrid (formerly gym-minigrid) + +[](https://pre-commit.com/) +[](https://github.com/psf/black) + +There are other gridworld Gym environments out there, but this one is +designed to be particularly simple, lightweight and fast. The code has very few +dependencies, making it less likely to break or fail to install. It loads no +external sprites/textures, and it can run at up to 5000 FPS on a Core i7 +laptop, which means you can run your experiments faster. A known-working RL +implementation can be found [in this repository](https://github.com/lcswillems/torch-rl). + +Requirements: +- Python 3.7 to 3.10 +- OpenAI Gym v0.26 +- NumPy 1.18+ +- Matplotlib (optional, only needed for display) - 3.0+ + +Please use this bibtex if you want to cite this repository in your publications: + +``` +@misc{gym_minigrid, + author = {Chevalier-Boisvert, Maxime and Willems, Lucas and Pal, Suman}, + title = {Minimalistic Gridworld Environment for OpenAI Gym}, + year = {2018}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/maximecb/gym-minigrid}}, +} +``` + +List of publications & submissions using MiniGrid or BabyAI (please open a pull request to add missing entries): +- [History Compression via Language Models in Reinforcement Learning.](https://proceedings.mlr.press/v162/paischer22a.html) (Johannes Kepler University Linz, PMLR 2022) +- [Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity](https://arxiv.org/abs/2202.02886) (Arizona State University, ICML 2022) +- [How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation](https://proceedings.mlr.press/v162/mavor-parker22a.html) (University College London, Boston University, ICML 2022) +- [In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications](https://openreview.net/pdf?id=rUwm9wCjURV) (Imperial College London, ICLR 2022) +- [Interesting Object, Curious Agent: Learning Task-Agnostic Exploration](https://arxiv.org/abs/2111.13119) (Meta AI Research, NeurIPS 2021) +- [Safe Policy Optimization with Local Generalized Linear Function Approximations](https://arxiv.org/abs/2111.04894) (IBM Research, Tsinghua University, NeurIPS 2021) +- [A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning](https://arxiv.org/abs/2106.02097) (Mila, McGill University, NeurIPS 2021) +- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](http://www.ifaamas.org/Proceedings/aamas2021/pdfs/p1118.pdf) (Tufts University, SIFT, AAMAS 2021) +- [Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning](https://arxiv.org/abs/2102.04220) (UCL, AAMAS 2021) +- [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://openreview.net/forum?id=MtEE0CktZht) (Texas A&M University, Kuai Inc., ICLR 2021) +- [Adversarially Guided Actor-Critic](https://openreview.net/forum?id=_mQp5cr_iNy) (INRIA, Google Brain, ICLR 2021) +- [Information-theoretic Task Selection for Meta-Reinforcement Learning](https://papers.nips.cc/paper/2020/file/ec3183a7f107d1b8dbb90cb3c01ea7d5-Paper.pdf) (University of Leeds, NeurIPS 2020) +- [BeBold: Exploration Beyond the Boundary of Explored Regions](https://arxiv.org/pdf/2012.08621.pdf) (UCB, December 2020) +- [Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems](https://arxiv.org/abs/2010.08843) (McGill, October 2020) +- [Prioritized Level Replay](https://arxiv.org/pdf/2010.03934.pdf) (FAIR, October 2020) +- [AllenAct: A Framework for Embodied AI Research](https://arxiv.org/pdf/2008.12760.pdf) (Allen Institute for AI, August 2020) +- [Learning with AMIGO: Adversarially Motivated Intrinsic Goals](https://arxiv.org/pdf/2006.12122.pdf) (MIT, FAIR, ICLR 2021) +- [RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments](https://openreview.net/forum?id=rkg-TJBFPB) (FAIR, ICLR 2020) +- [Learning to Request Guidance in Emergent Communication](https://arxiv.org/pdf/1912.05525.pdf) (University of Amsterdam, Dec 2019) +- [Working Memory Graphs](https://arxiv.org/abs/1911.07141) (MSR, Nov 2019) +- [Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning](https://arxiv.org/pdf/1910.04040.pdf) (Oct 2019, University of Antwerp) +- [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck](https://arxiv.org/abs/1910.12911) (MSR, NeurIPS, Oct 2019) +- [Recurrent Independent Mechanisms](https://arxiv.org/pdf/1909.10893.pdf) (Mila, Sept 2019) +- [Learning Effective Subgoals with Multi-Task Hierarchical Reinforcement Learning](http://surl.tirl.info/proceedings/SURL-2019_paper_10.pdf) (Tsinghua University, August 2019) +- [Mastering emergent language: learning to guide in simulated navigation](https://arxiv.org/abs/1908.05135) (University of Amsterdam, Aug 2019) +- [Transfer Learning by Modeling a Distribution over Policies](https://arxiv.org/abs/1906.03574) (Mila, June 2019) +- [Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives](https://arxiv.org/abs/1906.10667) (Mila, June 2019) +- [Learning distant cause and effect using only local and immediate credit assignment](https://arxiv.org/abs/1905.11589) (Incubator 491, May 2019) +- [Practical Open-Loop Optimistic Planning](https://arxiv.org/abs/1904.04700) (INRIA, April 2019) +- [Learning World Graphs to Accelerate Hierarchical Reinforcement Learning](https://arxiv.org/abs/1907.00664) (Salesforce Research, 2019) +- [Variational State Encoding as Intrinsic Motivation in Reinforcement Learning](https://mila.quebec/wp-content/uploads/2019/05/WebPage.pdf) (Mila, TARL 2019) +- [Unsupervised Discovery of Decision States Through Intrinsic Control](https://tarl2019.github.io/assets/papers/modhe2019unsupervised.pdf) (Georgia Tech, TARL 2019) +- [Modeling the Long Term Future in Model-Based Reinforcement Learning](https://openreview.net/forum?id=SkgQBn0cF7) (Mila, ICLR 2019) +- [Unifying Ensemble Methods for Q-learning via Social Choice Theory](https://arxiv.org/pdf/1902.10646.pdf) (Max Planck Institute, Feb 2019) +- [Planning Beyond The Sensing Horizon Using a Learned Context](https://personalrobotics.cs.washington.edu/workshops/mlmp2018/assets/docs/18_CameraReadySubmission.pdf) (MLMP@IROS, 2018) +- [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882) (UC Berkeley, Nov 2018) +- [On the Complexity of Exploration in Goal-Driven Navigation](https://arxiv.org/abs/1811.06889) (CMU, NeurIPS, Nov 2018) +- [Transfer and Exploration via the Information Bottleneck](https://openreview.net/forum?id=rJg8yhAqKm) (Mila, Nov 2018) +- [Creating safer reward functions for reinforcement learning agents in the gridworld](https://gupea.ub.gu.se/bitstream/2077/62445/1/gupea_2077_62445_1.pdf) (University of Gothenburg, 2018) +- [BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop](https://arxiv.org/abs/1810.08272) (Mila, ICLR, Oct 2018) + +This environment has been built as part of work done at [Mila](https://mila.quebec). The Dynamic obstacles environment has been added as part of work done at [IAS in TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/) and the University of Genoa for mobile robot navigation with dynamic obstacles. + +## Installation + +There is now a [pip package](https://pypi.org/project/gym-minigrid/) available, which is updated periodically: + +``` +pip3 install gym-minigrid +``` + +Alternatively, to get the latest version of MiniGrid, you can clone this repository and install the dependencies with `pip3`: + +``` +git clone https://github.com/maximecb/gym-minigrid.git +cd gym-minigrid +pip3 install -e . +``` + + + +%prep +%autosetup -n gym-minigrid-1.2.2 + +%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-gym-minigrid -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.2-1 +- Package Spec generated @@ -0,0 +1 @@ +cea10430fa0523d274fe866a13cef05e gym_minigrid-1.2.2.tar.gz |