2006
NeurIPS
NeurIPS 2006
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning
Abstract
We present a learning algorithm for undiscounted reinforcement learning. Our interest lies in bounds for the algorithm's online performance after some finite number of steps. In the spirit of similar methods already successfully applied for the exploration-exploitation tradeoff in multi-armed bandit problems, we use upper confidence bounds to show that our UCRL algorithm achieves logarithmic online regret in the number of steps taken with respect to an optimal policy.
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Conference Pioneer
- NeurIPS 2006
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Interdisciplinary Bridge
- Artificial Intelligence and Reinforcement Learning
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Trend Setter
- Online Algorithms
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Keyword Pioneer
- optimal policy
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Hot Topic Early Bird
- multi-armed bandit
Authors
Topics
Machine Learning > Optimization & Theory > Learning Theory
Reinforcement Learning > Methods > Deep RL
Reinforcement Learning > Applications > Value Iteration
Machine Learning > Learning Types > Online Learning
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Optimization & Theory > Online Algorithms
Machine Learning > Learning Types > Multi-Armed Bandits
Artificial Intelligence > Core AI > Reinforcement Learning
Machine Learning > Learning Types > Exploration-Exploitation