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A Framework for History-Aware Hyperparameter Optimisation in Reinforcement Learning
A Reinforcement Learning (RL) system depends on a set of initial conditions (hyperparameters) that affect the system's performance.
RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation
Decision-making for self-adaptation approaches need to address different challenges, including the quantification of the uncertainty of events that cannot be foreseen in advance and their effects, and dealing with conflicting objectives that inherently involve multi-objective decision making (e. g., avoiding costs vs. providing reliable service).
Gamified and Self-Adaptive Applications for the Common Good: Research Challenges Ahead
In this paper we propose our vision on how the principles at the base of the autonomous and multi-agent systems can be exploited to design multi-challenge motivational systems to engage smart communities towards common goals.
