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111 lines
8.4 KiB
111 lines
8.4 KiB
Metadata-Version: 2.1
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Name: gym_minigrid
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Version: 1.2.2
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Summary: Minimalistic gridworld reinforcement learning environments
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Home-page: https://github.com/Farama-Foundation/gym-minigrid
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Author: Farama Foundation
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Author-email: jkterry@farama.org
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License: Apache
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Keywords: memory,environment,agent,rl,gym
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.7
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Classifier: Programming Language :: Python :: 3.8
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Classifier: Programming Language :: Python :: 3.9
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Classifier: Programming Language :: Python :: 3.10
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Requires-Python: >=3.7
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Description-Content-Type: text/markdown
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Provides-Extra: testing
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License-File: LICENSE
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# MiniGrid (formerly gym-minigrid)
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[](https://pre-commit.com/)
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[](https://github.com/psf/black)
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There are other gridworld Gym environments out there, but this one is
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designed to be particularly simple, lightweight and fast. The code has very few
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dependencies, making it less likely to break or fail to install. It loads no
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external sprites/textures, and it can run at up to 5000 FPS on a Core i7
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laptop, which means you can run your experiments faster. A known-working RL
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implementation can be found [in this repository](https://github.com/lcswillems/torch-rl).
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Requirements:
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- Python 3.7 to 3.10
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- OpenAI Gym v0.26
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- NumPy 1.18+
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- Matplotlib (optional, only needed for display) - 3.0+
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Please use this bibtex if you want to cite this repository in your publications:
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```
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@misc{gym_minigrid,
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author = {Chevalier-Boisvert, Maxime and Willems, Lucas and Pal, Suman},
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title = {Minimalistic Gridworld Environment for OpenAI Gym},
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year = {2018},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/maximecb/gym-minigrid}},
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}
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```
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List of publications & submissions using MiniGrid or BabyAI (please open a pull request to add missing entries):
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- [History Compression via Language Models in Reinforcement Learning.](https://proceedings.mlr.press/v162/paischer22a.html) (Johannes Kepler University Linz, PMLR 2022)
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- [Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity](https://arxiv.org/abs/2202.02886) (Arizona State University, ICML 2022)
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- [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)
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- [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)
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- [Interesting Object, Curious Agent: Learning Task-Agnostic Exploration](https://arxiv.org/abs/2111.13119) (Meta AI Research, NeurIPS 2021)
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- [Safe Policy Optimization with Local Generalized Linear Function Approximations](https://arxiv.org/abs/2111.04894) (IBM Research, Tsinghua University, NeurIPS 2021)
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- [A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning](https://arxiv.org/abs/2106.02097) (Mila, McGill University, NeurIPS 2021)
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- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](http://www.ifaamas.org/Proceedings/aamas2021/pdfs/p1118.pdf) (Tufts University, SIFT, AAMAS 2021)
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- [Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning](https://arxiv.org/abs/2102.04220) (UCL, AAMAS 2021)
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- [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)
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- [Adversarially Guided Actor-Critic](https://openreview.net/forum?id=_mQp5cr_iNy) (INRIA, Google Brain, ICLR 2021)
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- [Information-theoretic Task Selection for Meta-Reinforcement Learning](https://papers.nips.cc/paper/2020/file/ec3183a7f107d1b8dbb90cb3c01ea7d5-Paper.pdf) (University of Leeds, NeurIPS 2020)
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- [BeBold: Exploration Beyond the Boundary of Explored Regions](https://arxiv.org/pdf/2012.08621.pdf) (UCB, December 2020)
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- [Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems](https://arxiv.org/abs/2010.08843) (McGill, October 2020)
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- [Prioritized Level Replay](https://arxiv.org/pdf/2010.03934.pdf) (FAIR, October 2020)
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- [AllenAct: A Framework for Embodied AI Research](https://arxiv.org/pdf/2008.12760.pdf) (Allen Institute for AI, August 2020)
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- [Learning with AMIGO: Adversarially Motivated Intrinsic Goals](https://arxiv.org/pdf/2006.12122.pdf) (MIT, FAIR, ICLR 2021)
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- [RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments](https://openreview.net/forum?id=rkg-TJBFPB) (FAIR, ICLR 2020)
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- [Learning to Request Guidance in Emergent Communication](https://arxiv.org/pdf/1912.05525.pdf) (University of Amsterdam, Dec 2019)
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- [Working Memory Graphs](https://arxiv.org/abs/1911.07141) (MSR, Nov 2019)
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- [Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning](https://arxiv.org/pdf/1910.04040.pdf) (Oct 2019, University of Antwerp)
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- [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck](https://arxiv.org/abs/1910.12911) (MSR, NeurIPS, Oct 2019)
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- [Recurrent Independent Mechanisms](https://arxiv.org/pdf/1909.10893.pdf) (Mila, Sept 2019)
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- [Learning Effective Subgoals with Multi-Task Hierarchical Reinforcement Learning](http://surl.tirl.info/proceedings/SURL-2019_paper_10.pdf) (Tsinghua University, August 2019)
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- [Mastering emergent language: learning to guide in simulated navigation](https://arxiv.org/abs/1908.05135) (University of Amsterdam, Aug 2019)
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- [Transfer Learning by Modeling a Distribution over Policies](https://arxiv.org/abs/1906.03574) (Mila, June 2019)
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- [Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives](https://arxiv.org/abs/1906.10667) (Mila, June 2019)
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- [Learning distant cause and effect using only local and immediate credit assignment](https://arxiv.org/abs/1905.11589) (Incubator 491, May 2019)
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- [Practical Open-Loop Optimistic Planning](https://arxiv.org/abs/1904.04700) (INRIA, April 2019)
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- [Learning World Graphs to Accelerate Hierarchical Reinforcement Learning](https://arxiv.org/abs/1907.00664) (Salesforce Research, 2019)
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- [Variational State Encoding as Intrinsic Motivation in Reinforcement Learning](https://mila.quebec/wp-content/uploads/2019/05/WebPage.pdf) (Mila, TARL 2019)
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- [Unsupervised Discovery of Decision States Through Intrinsic Control](https://tarl2019.github.io/assets/papers/modhe2019unsupervised.pdf) (Georgia Tech, TARL 2019)
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- [Modeling the Long Term Future in Model-Based Reinforcement Learning](https://openreview.net/forum?id=SkgQBn0cF7) (Mila, ICLR 2019)
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- [Unifying Ensemble Methods for Q-learning via Social Choice Theory](https://arxiv.org/pdf/1902.10646.pdf) (Max Planck Institute, Feb 2019)
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- [Planning Beyond The Sensing Horizon Using a Learned Context](https://personalrobotics.cs.washington.edu/workshops/mlmp2018/assets/docs/18_CameraReadySubmission.pdf) (MLMP@IROS, 2018)
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- [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882) (UC Berkeley, Nov 2018)
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- [On the Complexity of Exploration in Goal-Driven Navigation](https://arxiv.org/abs/1811.06889) (CMU, NeurIPS, Nov 2018)
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- [Transfer and Exploration via the Information Bottleneck](https://openreview.net/forum?id=rJg8yhAqKm) (Mila, Nov 2018)
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- [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)
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- [BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop](https://arxiv.org/abs/1810.08272) (Mila, ICLR, Oct 2018)
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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.
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## Installation
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There is now a [pip package](https://pypi.org/project/gym-minigrid/) available, which is updated periodically:
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```
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pip3 install gym-minigrid
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```
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Alternatively, to get the latest version of MiniGrid, you can clone this repository and install the dependencies with `pip3`:
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```
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git clone https://github.com/maximecb/gym-minigrid.git
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cd gym-minigrid
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pip3 install -e .
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```
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