Advancements in Reinforcement Learning and Information Freshness

The field of reinforcement learning and information freshness is rapidly evolving, with a focus on developing innovative methods to improve decision-making in complex and dynamic environments. Researchers are exploring new approaches to address the challenges of information staleness, out-of-distribution actions, and state distributional shift. Notably, there is a growing interest in designing algorithms that can balance exploration and exploitation, and in developing frameworks that can quantify the value of information in decision-critical settings. Some notable papers in this area are:

  • FAST-Q, which introduces a novel approach to counterfactual action estimation in offline reinforcement learning.
  • Variational OOD State Correction for Offline Reinforcement Learning, which proposes a method to correct out-of-distribution states in offline reinforcement learning.
  • Q-function Decomposition with Intervention Semantics, which studies the condition for the unbiasedness of decomposed Q-functions using causal effect estimation.

Sources

Information Freshness in Dynamic Gossip Networks

Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments

\textit{From Freshness to Effectiveness}: Goal-Oriented Sampling for Remote Decision Making

Q-function Decomposition with Intervention Semantics with Factored Action Spaces

FAST-Q: Fast-track Exploration with Adversarially Balanced State Representations for Counterfactual Action Estimation in Offline Reinforcement Learning

Variational OOD State Correction for Offline Reinforcement Learning

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