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Evolutionary Reinforcement Learning in Relational Domains

Current reinforcement learning (Kaelbling et al., 1996) (RL) research is largely concerned with structure and generalization in sequential learning tasks. For example, hierarchical approaches focus on task structure whereas function approximators exploit structural aspects of states to compress representations and speed up learning. A recent direction is the use of relational representations that employ generalization over objects and relations in the environment (see van Otterlo (2005) for a detailed survey). The work on relational RL so far has been mainly concerned with learning value functions although they are hard to represent and learn in relational settings. In this paper we introduce Grey, a new evolutionary method searching directly in policy space for relational domains.