Towards Interpretable Multi-task Learning Using Bilevel Programming

Solving multi objective linear Bilevel multi follower programming problem

Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning

Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to employ hierarchical policies that decide when to use a previously learned policy a...

solving multi objective linear bilevel multi follower programming problem

Multi-Task Learning via Conic Programming

When we have several related tasks, solving them simultaneously is shown to be more effective than solving them individually. This approach is called multi-task learning (MTL) and has been studied extensively. Existing approaches to MTL often treat all the tasks as uniformly related to each other and the relatedness of the tasks is controlled globally. For this reason, the existing methods can ...

A New Method For Solving Linear Bilevel Multi-Objective Multi-Follower Programming Problem

Linear bilevel programming is a decision making problem with a two-level decentralized organization. The leader is in the upper level and the follower, in the lower level. This study addresses linear bilevel multi-objective multi-follower programming (LB-MOMFP) problem, a special case of linear bilevel programming problems with one leader and multiple followers where each decision maker has sev...