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Skip to Search Results- 3Online learning
- 2Artificial Intelligence
- 2Machine Learning
- 1Active learning
- 1Actor-critic methods
- 1Bias-variance tradeoff
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2004
Technical report TR04-11. Learning in a multiagent system is a challenging problem due to two key factors. First, if other agents are simultaneously learning then the environment is no longer stationary, thus undermining convergence guarantees. Second, learning is often susceptible to...
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2012
Bowling, Michael, Zinkevich, Martin
Online learning aims to perform nearly as well as the best hypothesis in hindsight. For some hypothesis classes, though, even finding the best hypothesis offline is challenging. In such offline cases, local search techniques are often employed and only local optimality guaranteed. For online...
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2009
Technical report TR09-13. This article presents a survey of reinforcement learning algorithms for Markov Decision Processes (MDP). In the first half of the article, the problem of value estimation is considered. Here we start by describing the idea of bootstrapping and temporal difference...