124 citations · 190 across the 20 of their papers we have counts for
10 papers · 1 filter
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…
The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning
Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Christos Peridis +4
This paper introduces a set of formally defined and transparent problems for reinforcement learning algorithms with the following characteristics: (1) variable degrees of observabi…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
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…
Wasserstein Task Embedding for Measuring Task Similarities
Xinran Liu, Yikun Bai, Yuzhe Lu +2
Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most curr…
SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings
Yuzhe Lu, Xinran Liu, Andrea Soltoggio +1
Learning from set-structured data is an essential problem with many applications in machine learning and computer vision. This paper focuses on non-parametric and data-independent…
Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture
Pawel Ladosz, Eseoghene Ben-Iwhiwhu, Jeffery Dick +6
This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA). The hy…