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cs.LG2024
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
Matthew Riemer, Gopeshh Subbaraj, Glen Berseth +1
Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectively minimize regret. However, recent advan…
cs.LG2024
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother +3
In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Traditionally, IRL is treated as an adversaria…