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@@ -11,11 +11,11 @@
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<link rel="index" title="Index" href="../../genindex/" /><link rel="search" title="Search" href="../../search/" /><link rel="next" title="Tutorial on Creating Environments" href="../create_env_tutorial/" /><link rel="prev" title="Basic Usage" href="../basic_usage/" />
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<link rel="index" title="Index" href="../../genindex/" /><link rel="search" title="Search" href="../../search/" /><link rel="next" title="Tutorial on Creating Environments" href="../create_env_tutorial/" /><link rel="prev" title="Basic Usage" href="../basic_usage/" />
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<link rel="canonical" href="https://minigrid.farama.org/content/publications.html" />
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<link rel="canonical" href="https://minigrid.farama.org/content/publications.html" />
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+ <link rel="shortcut icon" href="../../_static/minigrid-favicon.png"/><!-- Generated with Sphinx 7.2.6 and Furo 2023.08.19.dev1 -->
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<title>List of Publications - MiniGrid Documentation</title>
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<title>List of Publications - MiniGrid Documentation</title>
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<p class="caption" role="heading"><span class="caption-text">Introduction</span></p>
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<p class="caption" role="heading"><span class="caption-text">Introduction</span></p>
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<ul class="current">
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<ul class="current">
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<li class="toctree-l1"><a class="reference internal" href="../basic_usage/">Basic Usage</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../basic_usage/">Basic Usage</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../basic_usage/#training-an-agent">Training an Agent</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../basic_usage/#installation">Installation</a></li>
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<li class="toctree-l1 current current-page"><a class="current reference internal" href="#">List of Publications</a></li>
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<li class="toctree-l1 current current-page"><a class="current reference internal" href="#">List of Publications</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../create_env_tutorial/">Tutorial on Creating Environments</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../create_env_tutorial/">Tutorial on Creating Environments</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../training/">Training Minigrid Environments</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../training/">Training Minigrid Environments</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../training/#create-custom-feature-extractor">Create Custom Feature Extractor</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../training/#train-a-ppo-agent">Train a PPO Agent</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../training/#further-reading">Further Reading</a></li>
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</ul>
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</ul>
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<p class="caption" role="heading"><span class="caption-text">Wrappers</span></p>
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<p class="caption" role="heading"><span class="caption-text">Wrappers</span></p>
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<li class="toctree-l2"><a class="reference internal" href="../../api/wrappers/#direction-obs">Direction Obs</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../api/wrappers/#fully-obs">Fully Obs</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../api/wrappers/#image-observation">Image Observation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../api/wrappers/#no-death">No Death</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../api/wrappers/#no-death">No Death</a></li>
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- <section id="list-of-publications">
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-<h1>List of Publications<a class="headerlink" href="#list-of-publications" title="Permalink to this heading">#</a></h1>
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+ <section class="tex2jax_ignore mathjax_ignore" id="list-of-publications">
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+<h1>List of Publications<a class="headerlink" href="#list-of-publications" title="Link to this heading">#</a></h1>
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<p>List of publications & submissions using Minigrid or BabyAI (please open a pull request to add missing entries):</p>
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<p>List of publications & submissions using Minigrid or BabyAI (please open a pull request to add missing entries):</p>
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<ul class="simple">
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<ul class="simple">
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<li><p><a class="reference external" href="https://arxiv.org/abs/2205.15752">Hierarchies of Reward Machines</a> (Imperial College London, ILASP, Universitat Pompeu Fabra, ICML 2023)</p></li>
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<li><p><a class="reference external" href="https://arxiv.org/abs/2205.15752">Hierarchies of Reward Machines</a> (Imperial College London, ILASP, Universitat Pompeu Fabra, ICML 2023)</p></li>
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<li><p><a class="reference external" href="https://arxiv.org/abs/2301.10119">Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning</a> (Mila, McGill University, CoLLAs 2023)</p></li>
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<li><p><a class="reference external" href="https://arxiv.org/abs/2301.10119">Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning</a> (Mila, McGill University, CoLLAs 2023)</p></li>
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<li><p><a class="reference external" href="https://arxiv.org/abs/2304.10770">DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards</a> (U-Tokyo, Google Brain, IJCAI 2023)</p></li>
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<li><p><a class="reference external" href="https://arxiv.org/abs/2304.10770">DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards</a> (U-Tokyo, Google Brain, IJCAI 2023)</p></li>
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+<li><p><a class="reference external" href="https://arxiv.org/abs/2301.13136">Contrastive Meta-Learning for Partially Observable Few-Shot Learning</a> (University of Edinburgh, Microsoft Research Cambridge, ICLR 2023)</p></li>
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<li><p><a class="reference external" href="https://arxiv.org/abs/2211.16838">Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation</a> (TECNALIA, IEEE ADPRL 2022)</p></li>
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<li><p><a class="reference external" href="https://arxiv.org/abs/2211.16838">Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation</a> (TECNALIA, IEEE ADPRL 2022)</p></li>
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<li><p><a class="reference external" href="https://arxiv.org/abs/2205.11184">An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments</a> (TECNALIA, CD-MAKE 2022)</p></li>
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<li><p><a class="reference external" href="https://arxiv.org/abs/2205.11184">An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments</a> (TECNALIA, CD-MAKE 2022)</p></li>
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<li><p><a class="reference external" href="https://link.springer.com/chapter/10.1007/978-3-031-22695-3_19">Evolution Strategies for Sparse Reward Gridworld Environments.</a> (DSTG, AJCAI 2022)</p></li>
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<li><p><a class="reference external" href="https://link.springer.com/chapter/10.1007/978-3-031-22695-3_19">Evolution Strategies for Sparse Reward Gridworld Environments.</a> (DSTG, AJCAI 2022)</p></li>
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