Reinforcement Learning
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
In this paper we consider multi-objective reinforcement learning where the objectives are balanced using preferences.
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
In this paper we consider multi-objective reinforcement learning where the objectives are balanced using preferences.
Outcome-Driven Reinforcement Learning via Variational Inference Tim G. J. Rudner University of Oxford Vitchyr H. Pong
Illustration of the shaping effect of the reward function derived from the goal-directed variational inference objective.
Online Decision Based Visual Tracking via Reinforcement Learning
A deep visual tracker is typically based on either object detection or template matching while each of them is only suitable for a particular group of scenes. It is straightforward to consider fusing them together to pursue more reliable tracking. However, this is not wise as they follow different tracking principles.
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Typically these assumptions are stronger than what is needed to find good solutions in the batch setting.