Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Bayesian sparse sampling for on-line reward optimization
ICML '05 Proceedings of the 22nd international conference on Machine learning
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
Proceedings of the 25th international conference on Machine learning
Online kernel selection for Bayesian reinforcement learning
Proceedings of the 25th international conference on Machine learning
A Bayesian approach to imitation in reinforcement learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Improving reinforcement learning by using sequence trees
Machine Learning
Autonomous discovery of subgoals using acyclic state trajectories
ICICA'10 Proceedings of the First international conference on Information computing and applications
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Agent completely depends on trail-and-error to learn the optimal policy is the major reason to make reinforcement learning being slow and time consuming. Excepting for trail-and-error, human can also take advantage of prior learned experience to plan and accelerate subsequent learning. We propose an approach to model agent's learning experience by Bayesian Network, which can be used to shape agent for bias exploration towards the most promising regions of state space and thereby reduces exploration and accelerate learning. The experiment results on Grid-World problem show our approach can significantly improve agent's performance and shorten learning time. More importantly, our approach makes agent can take advantage of its learning experience to plan and accelerate learning.