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Learning Agents with Evolving Hypothesis Classes.

, and . AGI, volume 7999 of Lecture Notes in Computer Science, page 150-159. Springer, (2013)

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Wearable sensor activity analysis using semi-Markov models with a grammar., , , , , , , , and . Pervasive Mob. Comput., 6 (3): 342-350 (2010)Feature Reinforcement Learning using Looping Suffix Trees., , and . EWRL, volume 24 of JMLR Proceedings, page 11-24. JMLR.org, (2012)Q-learning for history-based reinforcement learning., , and . ACML, volume 29 of JMLR Workshop and Conference Proceedings, page 213-228. JMLR.org, (2013)Recursive channel selection techniques for brain computer interfaces., , and . EMBC, page 1753-1756. IEEE, (2012)Learning Agents with Evolving Hypothesis Classes., and . AGI, volume 7999 of Lecture Notes in Computer Science, page 150-159. Springer, (2013)Optimistic Agents Are Asymptotically Optimal., and . Australasian Conference on Artificial Intelligence, volume 7691 of Lecture Notes in Computer Science, page 15-26. Springer, (2012)Feature Reinforcement Learning in Practice., , and . EWRL, volume 7188 of Lecture Notes in Computer Science, page 66-77. Springer, (2011)Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems., , , , , , , , and . ALIFE, page 103-110. MIT Press, (2019)Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition., , , , and . AAMAS, page 2827-2829. ACM, (2023)Malthusian Reinforcement Learning., , , , , , , , and . AAMAS, page 1099-1107. International Foundation for Autonomous Agents and Multiagent Systems, (2019)