6 citations · 7 across the 4 of their papers we have counts for
5 papers
How to Find and Reuse Policies for Continuous Adaptation in Lifelong Reinforcement Learning
Saptarshi Nath, Inish M. D'Souza, Antonio Carta +2
In lifelong reinforcement learning, retaining previously learned policies is not sufficient for effective transfer to a new task. Useful knowledge may be distributed across several…
Policy Search, Retrieval, and Composition via Task Similarity in Collaborative Agentic Systems
Saptarshi Nath, Christos Peridis, Eseoghene Benjamin +7
Agentic AI aims to create systems that set their own goals, adapt proactively to change, and refine behavior through continuous experience. Recent advances suggest that, when facin…
Statistical Context Detection for Deep Lifelong Reinforcement Learning
Jeffery Dick, Saptarshi Nath, Christos Peridis +3
Context detection involves labeling segments of an online stream of data as belonging to different tasks. Task labels are used in lifelong learning algorithms to perform consolidat…
Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks
Saptarshi Nath, Christos Peridis, Eseoghene Ben-Iwhiwhu +5
Lifelong learning agents aim to learn multiple tasks sequentially over a lifetime. This involves the ability to exploit previous knowledge when learning new tasks and to avoid forg…
Lifelong Reinforcement Learning with Modulating Masks
Eseoghene Ben-Iwhiwhu, Saptarshi Nath, Praveen K. Pilly +2
Lifelong learning aims to create AI systems that continuously and incrementally learn during a lifetime, similar to biological learning. Attempts so far have met problems, includin…