Amber Cassimon

Amber Cassimon, Reinout Eyckerman, Siegfried Mercelis et al.

In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be quite basic, and not always representative of practical Multi-Objective Optimization problems. In an attempt to bring theory closer to practice, the authors propose an alternative, improved version of the DST problem, and prove that some of the properties that simplify the original DST problem no longer hold. The authors also provide a reference implementation and perform a comparison between their implementation, and other existing open-source implementations of the problem. Finally, the authors also provide a complete Pareto-front for their new DST problem.

Amber Cassimon, Siegfried Mercelis, Kevin Mets

Recently, a novel paradigm has been proposed for reinforcement learning-based NAS agents, that revolves around the incremental improvement of a given architecture. We assess the abilities of such reinforcement learning agents to transfer between different tasks. We perform our evaluation using the Trans-NASBench-101 benchmark, and consider the efficacy of the transferred agents, as well as how quickly they can be trained. We find that pretraining an agent on one task benefits the performance of the agent in another task in all but 1 task when considering final performance. We also show that the training procedure for an agent can be shortened significantly by pretraining it on another task. Our results indicate that these effects occur regardless of the source or target task, although they are more pronounced for some tasks than for others. Our results show that transfer learning can be an effective tool in mitigating the computational cost of the initial training procedure for reinforcement learning-based NAS agents.