average reward
Bandits attack function optimization
Preux, Philippe, Munos, Rémi, Valko, Michal
We consider function optimization as a sequential decision making problem under budget constraint. This constraint limits the number of objective function evaluations allowed during the optimization. We consider an algorithm inspired by a continuous version of a multi-armed bandit problem which attacks this optimization problem by solving the tradeoff between exploration (initial quasi-uniform search of the domain) and exploitation (local optimization around the potentially global maxima). We introduce the so-called Simultaneous Optimistic Optimization (SOO), a deterministic algorithm that works by domain partitioning. The benefit of such approach are the guarantees on the returned solution and the numerical efficiency of the algorithm. We present this machine learning approach to optimization, and provide the empirical assessment of SOO on the CEC'2014 competition on single objective real-parameter numerical optimization test-suite.
Regret Bounds for Learning State Representations in Reinforcement Learning
Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard
Neural Information Processing Systems http://nips.cc/
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments
Mahdi Imani, Seyede Fatemeh Ghoreishi, Ulisses M. Braga-Neto
Neural Information Processing Systems http://nips.cc/
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning
Romain WARLOP, Alessandro Lazaric, Jérémie Mary
We argue that this assumption is rarely verified in practice, as the recommendation process itself may impact the user's preferences.
Appendix: Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
Thus the optimal average reward of the original MDP and modified MDP differ by O ( ϵ). To ensure Assumption 3.1 (b) is satisfied, an aperiodicity transformation can be implemented. The proof of this theorem can be found in [Sch71]. From Lemma 2.2, we thus have, ( J In order to iterate Equation (8), need to ensure the terms are non-negative. Theorem 3.3 presents an upper bound on the error in terms of the average reward.
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
Reinforcement Learning algorithms can be broadly classified into value-based methods and policy-based methods.
R-learninginactor-criticmodeloffersabiologically relevantmechanismforsequentialdecision-making
Afewstudies haveexplored sequential stay-or-leavedecisions in humans, or rodents - the model organism used to access neuronal activity at high resolution. In both cases, decision patterns were collected inforaging tasks-the experimental settings where subjects decide when to leave depleting resources (2).
A Proofs from Section 2 448 Algorithm 4: Output ˆ α null G1 (1 η
Return ˆ α We show the following generalization of Proposition 2.1. Moreover, Alg. 4 has sample complexity The sample complexity is clear so we focus on the first statement. Theorem 4.5 in [MU17]) on these events as i varies and noting that Hence recalling (A.2) above, we conclude that The other direction is similar. Using (A.2) in the same way as above, we find First we analyze the expected sample complexity. Finally Alg. 4 has sample complexity We do this using Bayes' rule.