Artificial Intelligence - Special issue on knowledge representation
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An analytic solution to discrete Bayesian reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Planning and acting in partially observable stochastic domains
Artificial Intelligence
A POMDP approximation algorithm that anticipates the need to observe
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
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In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. In such cases, a common solution approach is to compute an approximation of the value function in terms of state features. However, relatively little attention has been paid to the cost of computing these state features. For example, search-based features may be useful for value prediction, but their computational cost must be traded off with their impact on value accuracy. To this end, we introduce a new cost-sensitive sparse linear regression paradigm for value function approximation in reinforcement learning where the learner is able to select only those costly features that are sufficiently informative to justify their computation. We illustrate the learning behavior of our approach using a simple experimental domain that allows us to explore the effects of a range of costs on the cost-performance trade-off.