Rule-based interactive assisted reinforcement learning

Reinforcement Learning (RL) has seen increasing interest over the past few years, partially owing to breakthroughs in the digestion and application of external information. The use of external information results in improved learning speeds and solutions to more complex domains. This thesis, a collection of five key contributions, demonstrates that comparable performance gains to existing Interactive Reinforcement Learning methods can be achieved using less data, sourced during operation, and without prior verifcation and validation of the information's integrity. First, this thesis introduces Assisted Reinforcement Learning (ARL), a collective term referring to RL methods that utilise external information to leverage the learning process, and provides a non-exhaustive review of current ARL methods. Second, two advice delivery methods common in ARL, evaluative and informative, are compared through human trials. The comparison highlights how human engagement, accuracy of advice, agent performance, and advice utility differ between the two methods. Third, this thesis introduces simulated users as a methodology for testing and comparing ARL methods. Simulated users enable testing and comparing of ARL systems without costly and time-consuming human trials. While not a replacement for well-designed human trials, simulated users offer a cheap and robust approach to ARL design and comparison. Fourth, the concept of persistence is introduced to Interactive Reinforcement Learning. The retention and reuse of advice maximises utility and can lead to improved performance and reduced human demand. Finally, this thesis presents rule-based interactive RL, an iterative method for providing advice to an agent. Existing interactive RL methods rely on constant human supervision and evaluation, requiring a substantial commitment from the advice-giver. Rule-based advice can be provided proactively and be generalised over the state-space while remaining flexible enough to handle potentially inaccurate or irrelevant information. Ultimately, the thesis contributions are validated empirically and clearly show that rule-based advice signicantly reduces human guidance requirements while improving agent performance.Doctor of Pholosoph

Agent-Driven Representations, Algorithms, and Metrics for Automated Organizational Design.

As cooperative multiagent systems (MASs) increase in interconnectivity, complexity, size, and longevity, coordinating the agents' reasoning and behaviors becomes increasingly difficult. One approach to address these issues is to use insights from human organizations to design structures within which the agents can more efficiently reason and interact. Generally speaking, an organization influences each agent such that, by following its respective influences, an agent can make globally-useful local decisions without having to explicitly reason about the complete joint coordination problem. For example, an organizational influence might constrain and/or inform which actions an agent performs. If these influences are well-constructed to be cohesive and correlated across the agents, then each agent is influenced into reasoning about and performing only the actions that are appropriate for its (organizationally-designated) portion of the joint coordination problem. In this dissertation, I develop an agent-driven approach to organizations, wherein the foundation for representing and reasoning about an organization stems from the needs of the agents in the MAS. I create an organizational specification language to express the possible ways in which an organization could influence the agents' decision making processes, and leverage details from those decision processes to establish quantitative, principled metrics for organizational performance based on the expected impact that an organization will have on the agents' reasoning and behaviors. Building upon my agent-driven organizational representations, I identify a strategy for automating the organizational design process~(ODP), wherein my ODP computes a quantitative description of organizational patterns and then searches through those possible patterns to identify an (approximately) optimal set of organizational influences for the MAS. Evaluating my ODP reveals that it can create organizations that both influence the MAS into effective patterns of joint policies and also streamline the agents' decision making in a coordinate manner. Finally, I use my agent-driven approach to identify characteristics of effective abstractions over organizational influences and a heuristic strategy for converging on a good abstraction.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113616/1/jsleight_1.pd