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Found 4 papers, 0 papers with code
Dynamic Knowledge Injection for AIXI Agents
Prior approximations of AIXI, a Bayesian optimality notion for general reinforcement learning, can only approximate AIXI's Bayesian environment model using an a-priori defined set of models.
A Direct Approximation of AIXI Using Logical State Abstractions
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.
Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces
This paper introduces the factored conditional filter, a new filtering algorithm for simultaneously tracking states and estimating parameters in high-dimensional state spaces.
Conditions on Features for Temporal Difference-Like Methods to Converge
The convergence of many reinforcement learning (RL) algorithms with linear function approximation has been investigated extensively but most proofs assume that these methods converge to a unique solution.
