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cs.LG2026
Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning
Noah Farr, Aryaman Reddi, Carlo D'Eramo +1
Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process data incrementally, i.e. with…
cs.LG2025
Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization
Sebastian Griesbach, Carlo D'Eramo
Numerous heuristics and advanced approaches have been proposed for exploration in different settings for deep reinforcement learning. Noise-based exploration generally fares well w…
cs.LG2025
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…