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Accelerating reinforcement learning through imitation

University of British Columbia

Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviours by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. Using reinforcement learning theory, we construct a new, formal framework for imitation that permits agents to combine prior knowledge, learned knowledge and knowledge extracted from observations of other agents. This framework, which we call implicit imitation, uses observations of other agents to provide an observer agent with information about its action capabilities in unexperienced situations. Efficient algorithms are derived from this framework for agents with both similar and dissimilar action capabilities and a series of experiments demonstrate that implicit imitation can dramatically accelerate reinforcement learning in certain cases. Further experiments demonstrate that the framework can handle transfer between agents with different reward structures, learning from multiple mentors, and selecting relevant portions of examples. A Bayesian version of implicit imitation is then derived and several experiments are used to illustrate how it smoothly integrates model extraction with prior knowledge and optimal exploration. Initial work is also presented to illustrate how extensions to implicit imitation can be derived for continuous domains, partially observable environments and multi-agent Markov games. The derivation of algorithms and experimental results are supplemented with an analysis of relevant domain features for imitating agents and some performance metrics for predicting imitation gains are suggested.

Thesis/Dissertation

application/pdf

2009-11-13

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http://hdl.handle.net/2429/14937

Computer Science

University of British Columbia

UBCV

DSpace