A Direct Approximation of AIXI Using Logical State Abstractions

• URL: http://arxiv.org/abs/2210.06917v1
• Date: Thu, 13 Oct 2022 11:30:56 GMT
• Title: A Direct Approximation of AIXI Using Logical State Abstractions
• Authors: Samuel Yang-Zhao, Tianyu Wang, Kee Siong Ng
• Abstract summary: We propose a practical integration of logical state abstraction with AIXI, a Bayesian optimality notion for reinforcement learning agents. We address the problem of selecting the right subset of features to form state abstractions. Exact Bayesian model learning is then achieved using a suitable generalisation of Context Tree Weighting over abstract state sequences.
• Score: 6.570488724773507
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose a practical integration of logical state abstraction with AIXI, a Bayesian optimality notion for reinforcement learning agents, to significantly expand the model class that AIXI agents can be approximated over to complex history-dependent and structured environments. The state representation and reasoning framework is based on higher-order logic, which can be used to define and enumerate complex features on non-Markovian and structured environments. We address the problem of selecting the right subset of features to form state abstractions by adapting the $\Phi$-MDP optimisation criterion from state abstraction theory. Exact Bayesian model learning is then achieved using a suitable generalisation of Context Tree Weighting over abstract state sequences. The resultant architecture can be integrated with different planning algorithms. Experimental results on controlling epidemics on large-scale contact networks validates the agent's performance.

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