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We propose a practical integration of logical state abstraction with AIXI, aBayesian optimality notion for reinforcement learning agents, to significantlyexpand the model class that AIXI agents can be approximated over to complexhistory-dependent and structured environments. The state representation andreasoning framework is based on higher-order logic, which can be used to defineand enumerate complex features on non-Markovian and structured environments. Weaddress the problem of selecting the right subset of features to form stateabstractions by adapting the $\Phi$-MDP optimisation criterion from stateabstraction theory. Exact Bayesian model learning is then achieved using asuitable generalisation of Context Tree Weighting over abstract statesequences. The resultant architecture can be integrated with different planningalgorithms. Experimental results on controlling epidemics on large-scalecontact networks validates the agent's performance.

A Direct Approximation of AIXI Using Logical State Abstractions