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Found 3 papers, 2 papers with code
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems
We focus on situations where a conflict is detected between participants' choices and motivations, and propose methods for estimating value preferences while addressing detected inconsistencies by interacting with the participants.
MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response
This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption.
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning
Inferring reward functions from demonstrations and pairwise preferences are auspicious approaches for aligning Reinforcement Learning (RL) agents with human intentions.
