Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
Optimality issues of universal greedy agents with static priors
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Complexity-based induction systems: Comparisons and convergence theorems
IEEE Transactions on Information Theory
Universal knowledge-seeking agents
Theoretical Computer Science
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Finding the universal artificial intelligent agent is the old dream of AI scientists. Solomonoff Induction was one big step towards this, giving a universal solution to the general problem of sequence prediction by defining a universal prior distribution. Hutter defined the AIXI model, which extends the latter to the reinforcement learning framework, where almost all if not all AI problems can be formulated. However, new difficulties arise because the agent is now active, whereas it is only passive in the sequence prediction case. This makes proving AIXI's optimality difficult. In fact, we prove that the current definition of AIXI can sometimes be suboptimal in a certain sense, but that this behavior is still the most rational one, hence emphasizing the difficulty of universal reinforcement learning.