State Abstraction Discovery from Irrelevant State Variables

Abstraction is a powerful form of domain knowledge that allows reinforcement-learning agents to cope with complex environments, but in most cases a human must supply this knowledge. In the absence of such prior knowledge or a given model, we propose an algorithm for the automatic discovery of state abstraction from policies learned in one domain for use in other domains that have similar structure. To this end, we introduce a novel condition for state abstraction in terms of the relevance of state features to optimal behavior, and we exhibit statistical methods that detect this condition robustly. Finally, we show how to apply temporal abstraction to benefit safely from even partial state abstraction in the presence of generalization error.ion is a powerful form of domain knowledge that allows reinforcement-learning agents to cope with complex environments, but in most cases a human must supply this knowledge. In the absence of such prior knowledge or a given model, we propose an algorithm for the automatic discovery of state abstraction from policies learned in one domain for use in other domains that have similar structure. To this end, we introduce a novel condition for state abstraction in terms of the relevance of state features to optimal behavior, and we exhibit statistical methods that detect this condition robustly. Finally, we show how to apply temporal abstraction to benefit safely from even partial state abstraction in the presence of generalization error.