{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/152649"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/152649","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Information-theoretic Algorithms for Model-free Reinforcement Learning","abstract":"In this work, we propose a model-free reinforcement learning algorithm for infinte-horizon, average-reward decision processes where the transition function has a finite yet unknown dependence on history, and where the induced Markov Decision Process is assumed to be weakly communicating. This algorithm combines the Lempel-Ziv (LZ) parsing tree structure for states introduced in [4] together with the optimistic Q-learning approach in [9]. We mathematically analyze the algorithm towards showing sublinear regret, providing major steps towards the proof of such. In doing so, we reduce the proof to showing sub-linearity of a key quantity related to the sum of an uncertainty metric at each step. Simulations of the algorithm will be done in a later work.","abstract_html":"In this work, we propose a model-free reinforcement learning algorithm for infinte-horizon, average-reward decision processes where the transition function has a finite yet unknown dependence on history, and where the induced Markov Decision Process is assumed to be weakly communicating. This algorithm combines the Lempel-Ziv (LZ) parsing tree structure for states introduced in [4] together with the optimistic Q-learning approach in [9]. We mathematically analyze the algorithm towards showing sublinear regret, providing major steps towards the proof of such. In doing so, we reduce the proof to showing sub-linearity of a key quantity related to the sum of an uncertainty metric at each step. Simulations of the algorithm will be done in a later work.","abstract_has_math":false,"creators":["Wu, Farrell Eldrian S."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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This algorithm combines the Lempel-Ziv (LZ) parsing tree structure for states introduced in [4] together with the optimistic Q-learning approach in [9]. We mathematically analyze the algorithm towards showing sublinear regret, providing major steps towards the proof of such. In doing so, we reduce the proof to showing sub-linearity of a key quantity related to the sum of an uncertainty metric at each step. Simulations of the algorithm will be done in a later work."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Information-theoretic Algorithms for Model-free Reinforcement Learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Farias, Vivek F."],"dc:contributor.department":["Massachusetts Institute of Technology. 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