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Josiah Hanna
Ph.D
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I study a branch of machine learning called reinforcement learning (RL). Reinforcement learning allows autonomous agents to learn to complete sequential decision making tasks given only a reward signal and interaction with their environment. The goal of my research is to develop and apply reinforcement learning algorithms that are effective with a limited amount of time interacting with a task. My long-term research goal is to develop AI systems that can quickly master a new task with a small number of attempts at the task.
Papers25 papers
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NeurIPS, (2020)
ICML, pp.7543-7552, (2020)
AAAI, pp.565-572, (2019)
adaptive agents and multi-agents systems, pp.1016-1024, (2019)
national conference on artificial intelligence, (2019)
Cited by4Bibtex
AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems
Auckland
..., pp.88-96, (2019)
Autonomous Agents and Multi-Agent Systems, pp.88-96, (2019)
CoRR, no. 4 (2019): 6262-6269
AAAI Spring Symposia, (2018)
international conference on machine learning, (2018)
RoboCup, pp.45-58, (2017)
AAAI, pp.3834-3840, (2017)
AAMAS, pp.538-546, (2017)
Transportation Research Part C-emerging Technologies, (2017): 142-157
Cited by32Bibtex
AAMAS, pp.1834-1835, (2017)
ICML, (2017): 1394-1403
Guni Sharon,Josiah P. Hanna, Tarun Rambha, Michael W. Levin,Michael Albert,Stephen D. Boyles,Peter Stone
AAMAS '17 Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp.828-836, (2017)
IFAC-PapersOnLine, no. 15 (2016): 254-259
Cited by30Bibtex
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