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title: Publications
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# List of Publications


List of publications & submissions using Minigrid or BabyAI (please open a pull request to add missing entries):
- [Hierarchies of Reward Machines](https://arxiv.org/abs/2205.15752) (Imperial College London, ILASP, Universitat Pompeu Fabra, ICML 2023)
- [Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning](https://arxiv.org/abs/2301.10119) (Mila, McGill University, CoLLAs 2023)
- [DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards](https://arxiv.org/abs/2304.10770) (U-Tokyo, Google Brain, IJCAI 2023)
- [Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation](https://arxiv.org/abs/2211.16838) (TECNALIA, IEEE ADPRL 2022)
- [An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments](https://arxiv.org/abs/2205.11184) (TECNALIA, CD-MAKE 2022)
- [Evolution Strategies for Sparse Reward Gridworld Environments.](https://link.springer.com/chapter/10.1007/978-3-031-22695-3_19) (DSTG, AJCAI 2022)
- [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)
- [Compositional Generalization in Grounded Language Learning via Induced Model Sparsity](https://aclanthology.org/2022.naacl-srw.19/) (Aalto University, NAACL-SRW 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.