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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “reset-free”

Optimal control of the future via prospective learning with controlstat.ML

New method extends supervised learning for non-stationary control problems.

problem Optimal control in non-stationary, reset-free environments.

method Prospective Learning with Control (PLuC) using Empirical Risk Minimization (ERM).

result ERM asymptotically achieves Bayes optimal policy in non-stationary environments.

optimal control supervised learning reinforcement learning non-stationary reset-free

2025-11-11abs ↗pdf ↗

Online Markov Decision Processes with Terminal Law Constraintsmath.OC

New framework finds periodic policies in reset-free MDPs with sublinear regret.

problem Reset-free reinforcement learning with unknown dynamics and terminal law constraints.

method Periodic framework, periodic policies, periodic regret.

result First non-asymptotic guarantees for reset-free learning in multi-agent settings.

reinforcement learning Markov decision processes periodic policies terminal law constraints sublinear regret

2026-01-12abs ↗pdf ↗

Adaptive Online Planning for Continual Lifelong Learningcs.LG

AOP combines model-based planning with model-free learning to handle lifelong learning challenges.

problem Learning control in an online reset-free lifelong learning scenario where mistakes can compound and dynamics change.

method Adaptive Online Planning (AOP) that combines model-based planning with model-free learning, approximating uncertainty to call upon planning only when necessary.

result Achieves strong performance in lifelong learning challenges, gracefully adapting behaviors in the face of unpredictable changes.

lifelong learning online planning model-free learning model-based planning adaptive control

2019-12-03abs ↗pdf ↗

Optimal threshold resetting in collective diffusive searchcond-mat.stat-mech

Optimal threshold resetting reduces search time for multiple diffusive searchers.

problem Optimizing search time for multiple diffusive searchers in a one-dimensional space.

method Threshold resetting (TR) is introduced as an event-driven optimization strategy, coupling resetting to the internal dynamics of searchers.

result Optimal threshold distance

u

significantly reduces mean first-passage time for

N \geq 2

searchers, with a minimum at

N_{\mathrm{opt}}(u)

stochastic resetting diffusive search threshold resetting mean first-passage time non-interacting searchers

2026-03-26abs ↗pdf ↗