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cs.LG2026
Interactionless Inverse Reinforcement Learning: A Data-Centric Framework for Durable Alignment
Elias Malomgré, Pieter Simoens
AI alignment is growing in importance, yet many current approaches learn safety behavior by directly modifying policy parameters, entangling normative constraints with the underlyi…
cs.LG2025
Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning
Elias Malomgré, Pieter Simoens
Recent trends in Reinforcement Learning (RL) highlight the need for agents to learn from reward-free interactions and alternative supervision signals, such as unlabeled or incomple…