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Mixture Weighted Policy Cover: Exploration in Multi-Agent Reinforcement Learning

Miller, Dylan
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1651827594614518

Abstract Details

2022, Master of Science (M.S.), University of Dayton, Electrical Engineering.
Exploration plays a major role in the performance of reinforcement learning algorithms. Successful exploration should force the agent to access parts of the state-action space that it has not been heavily exposed to. This allows agents to find potentially better trajectories in terms of the value function that they yield. Exploration becomes much more difficult however when the environment is nonstationary. This is the case in multiagent reinforcement learning where other agents also learn and so change the dynamics of the environment from the perspective of any single agent. The upper confidence bound style reward bonus that is common in many reinforcement learning algorithms does not take this nonstationarity into account and therefore cannot be successfully applied to the multiagent setting. In this thesis, we propose Mixture-Weighted Policy Cover, a policy iteration algorithm using an upper confidence bound based intrinsic exploration bonus that encourages exploration in episodic multiagent settings by defining a policy cover that favors newer policies.
Raul Ordonez (Advisor)
Artificial Intelligence

Recommended Citations

Citations

  • Miller, D. (2022). Mixture Weighted Policy Cover: Exploration in Multi-Agent Reinforcement Learning [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1651827594614518

    APA Style (7th edition)

  • Miller, Dylan. Mixture Weighted Policy Cover: Exploration in Multi-Agent Reinforcement Learning. 2022. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1651827594614518.

    MLA Style (8th edition)

  • Miller, Dylan. "Mixture Weighted Policy Cover: Exploration in Multi-Agent Reinforcement Learning." Master's thesis, University of Dayton, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1651827594614518

    Chicago Manual of Style (17th edition)

dayton1651827594614518
112
© , some rights reserved.Creative Commons License Mixture Weighted Policy Cover: Exploration in Multi-Agent Reinforcement Learning by Dylan Miller is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Dayton and OhioLINK.