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Found 10 papers, 5 papers with code
Adaptive Foundation Models for Online Decisions: HyperAgent with Fast Incremental Uncertainty Estimation
Foundation models often struggle with uncertainty when faced with new situations in online decision-making, necessitating scalable and efficient exploration to resolve this uncertainty.
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation
This work advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs.
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization
The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics.
Probability Tools for Sequential Random Projection
We introduce the first probabilistic framework tailored for sequential random projection, an approach rooted in the challenges of sequential decision-making under uncertainty.
Simple, unified analysis of Johnson-Lindenstrauss with applications
We provide the first rigorous proof of the spherical construction's effectiveness and introduce a general class of sub-Gaussian constructions within this simplified framework.
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games
This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through bandit feedback alongside strategic decision-making.
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
We propose HyperAgent, a reinforcement learning (RL) algorithm based on the hypermodel framework for exploration in RL.
HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning
However, it is limited to the case where 1) a good feature is known in advance and 2) this feature is fixed during the training: if otherwise, RLSVI suffers an unbearable computational burden to obtain the posterior samples of the parameter in the $Q$-value function.
Divergence-Augmented Policy Optimization
In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data.
Hidden Community Detection in Social Networks
We introduce a new paradigm that is important for community detection in the realm of network analysis.
