@inproceedings{brunskill2013sample,
author = {Brunskill, Emma and Li, Lihong},
title = {Sample complexity of multi-task reinforcement learning},
booktitle = {Proceedings of the Twenty-Nineth Conference on Uncertainty in Artificial Intelligence (UAI-13)},
year = {2013},
month = {July},
abstract = {Transferring knowledge across a sequence of reinforcement-learning tasks is challenging, and has a number of important applications. Though there is encouraging empirical evidence that transfer can improve performance in subsequent reinforcement-learning tasks, there has been very little theoretical analysis. In this paper, we introduce a new multi-task algorithm for a sequence of reinforcement-learning tasks when each task is sampled independently from (an unknown) distribution over a finite set of Markov decision processes whose parameters are initially unknown. For this setting, we prove under certain assumptions that the per-task sample complexity of exploration is reduced significantly due to transfer compared to standard single-task algorithms. Our multi-task algorithm also has the desired characteristic that it is guaranteed not to exhibit negative transfer: in the worst case its per-task sample complexity is comparable to the corresponding single-task algorithm.},
publisher = {Association for Uncertainty in Artificial Intelligence},
url = {http://approjects.co.za/?big=en-us/research/publication/sample-complexity-of-multi-task-reinforcement-learning/},
edition = {Proceedings of the Twenty-Nineth Conference on Uncertainty in Artificial Intelligence (UAI-13)},
}