MaxHedge: Maximising a Maximum Online with Theoretical Performance Guarantees

Abstract We introduce a new online learning frame- work where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy budget. The goal is to maximise the cumulative profit, where the profit obtained on a single trial is defined as the difference between the maximum reward among the selected actions and the sum of their costs. Action energy values and the budget are known and fixed. All rewards and costs associated with each action change over time and are revealed at each trial only after the learner's selection of actions. Our framework encompasses several online learning problems where the environment changes over time; and the solution trades-off between minimising the costs and maximising the maximum reward of the selected subset of actions, while being con- strained to an action energy budget. The algorithm that we propose is an efficient and very scalable unifying approach which is capable of solving our general problem. Hence, our method solves several online learning problems which fall into this general frame- work.
  • Stephen Pasteris (UCL)
  • Fabio Vitale
  • Kevin Chan (ARL)
  • Shiqiang Wang (IBM US)
Date Oct-2018
Venue arxiv pre-print [link]