A practical guide to multi-objective reinforcement learning and planning von Hayes, Conor F., Rădulescu, Roxana, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa M., Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai A., Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik M.

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Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models von Roijers, Diederik M., Zintgraf, Luisa M., Libin, Pieter, Reymond, Mathieu, Bargiacchi, Eugenio, Nowé, Ann

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Interactive Thompson Sampling for Multi-objective Multi-armed Bandits von Roijers, Diederik M., Zintgraf, Luisa M., Nowé, Ann

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On the Practical Consistency of Meta-Reinforcement Learning Algorithms von Xiong, Zheng, Zintgraf, Luisa, Beck, Jacob, Vuorio, Risto, Whiteson, Shimon

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Optimizing piano practice with a utility-based scaffold von Moringen, Alexandra, Rüttgers, Sören, Zintgraf, Luisa, Friedman, Jason, Ritter, Helge

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A Survey of Meta-Reinforcement Learning von Beck, Jacob, Vuorio, Risto, Liu, Evan Zheran, Xiong, Zheng, Zintgraf, Luisa, Finn, Chelsea, Whiteson, Shimon

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Deep Interactive Bayesian Reinforcement Learning via Meta-Learning von Zintgraf, Luisa, Devlin, Sam, Ciosek, Kamil, Whiteson, Shimon, Hofmann, Katja

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Generalized Beliefs for Cooperative AI von Muglich, Darius, Zintgraf, Luisa, de Witt, Christian Schroeder, Whiteson, Shimon, Foerster, Jakob

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VIABLE: Fast Adaptation via Backpropagating Learned Loss von Feng, Leo, Zintgraf, Luisa, Peng, Bei, Whiteson, Shimon

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A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings von Ahmed, Eltayeb, Zintgraf, Luisa, de Witt, Christian A. Schroeder, Usunier, Nicolas

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Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients von Alizadeh, Milad, Tailor, Shyam A, Zintgraf, Luisa M, van Amersfoort, Joost, Farquhar, Sebastian, Lane, Nicholas Donald, Gal, Yarin

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Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning von Zintgraf, Luisa, Feng, Leo, Lu, Cong, Igl, Maximilian, Hartikainen, Kristian, Hofmann, Katja, Whiteson, Shimon

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A Survey of Meta-Reinforcement Learning von Beck, Jacob, Vuorio, Risto, Liu, Evan Zheran, Xiong, Zheng, Zintgraf, Luisa, Finn, Chelsea, Whiteson, Shimon

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Deep Interactive Bayesian Reinforcement Learning via Meta-Learning von Zintgraf, Luisa, Devlin, Sam, Ciosek, Kamil, Whiteson, Shimon, Hofmann, Katja

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INTERACTIVE AGENT von ZINTGRAF, Luisa, HOFMANN, Katja, CIOSEK, Kamil, DEVLIN, Sam

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ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition von Massiceti, Daniela, Zintgraf, Luisa, Bronskill, John, Theodorou, Lida, Matthew Tobias Harris, Cutrell, Edward, Morrison, Cecily, Hofmann, Katja, Stumpf, Simone

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VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning von Zintgraf, Luisa, Shiarlis, Kyriacos, Igl, Maximilian, Schulze, Sebastian, Gal, Yarin, Hofmann, Katja, Whiteson, Shimon

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Communicating via Markov Decision Processes von Sokota, Samuel, de Witt, Christian Schroeder, Igl, Maximilian, Zintgraf, Luisa, Torr, Philip, Strohmeier, Martin, Kolter, J. Zico, Whiteson, Shimon, Foerster, Jakob

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On the Practical Consistency of Meta-Reinforcement Learning Algorithms von Xiong, Zheng, Zintgraf, Luisa, Beck, Jacob, Vuorio, Risto, Whiteson, Shimon

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INTERACTIVE AGENT von HOFMANN, Katja, DEVLIN, Sam, Michael, ZINTGRAF, Luisa, Maria, CIOSEK, Kamil, Andrzej

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