TY - JOUR
T1 - Online stochastic shortest path with bandit feedback and unknown transition function
AU - Rosenberg, Aviv
AU - Mansour, Yishay
N1 - Publisher Copyright:
© 2019 Neural information processing systems foundation. All rights reserved.
PY - 2019
Y1 - 2019
N2 - We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes. The transition function is fixed but unknown to the learner, and the learner only observes bandit feedback (not the entire loss function). For this problem we develop no-regret algorithms that perform asymptotically as well as the best stationary policy in hindsight. Assuming that all states are reachable with probability ß > 0 under any policy, we give a regret bound of Õ(L|X|p|A|T/ß), where T is the number of episodes, X is the state space, A is the action space, and L is the length of each episode. When this assumption is removed we give a regret bound of Õ(L3/2|X||A|1/4T3/4), that holds for an arbitrary transition function. To our knowledge these are the first algorithms that in our setting handle both bandit feedback and an unknown transition function.
AB - We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes. The transition function is fixed but unknown to the learner, and the learner only observes bandit feedback (not the entire loss function). For this problem we develop no-regret algorithms that perform asymptotically as well as the best stationary policy in hindsight. Assuming that all states are reachable with probability ß > 0 under any policy, we give a regret bound of Õ(L|X|p|A|T/ß), where T is the number of episodes, X is the state space, A is the action space, and L is the length of each episode. When this assumption is removed we give a regret bound of Õ(L3/2|X||A|1/4T3/4), that holds for an arbitrary transition function. To our knowledge these are the first algorithms that in our setting handle both bandit feedback and an unknown transition function.
UR - http://www.scopus.com/inward/record.url?scp=85090176722&partnerID=8YFLogxK
M3 - מאמר מכנס
AN - SCOPUS:85090176722
VL - 32
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
SN - 1049-5258
Y2 - 8 December 2019 through 14 December 2019
ER -