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Found 7 papers, 2 papers with code
Rarest-First with Probabilistic-Mode-Suppression
In this work, we propose a tunable piece-selection policy that minimizes this (undesirable) requisite by combining the (work-conserving but not stabilizing) rarest-first protocol with only an appropriate share of the (non-work conserving and stabilizing) mode-suppression protocol.
OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation
This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms.
Learning a Discrete Set of Optimal Allocation Rules in Queueing Systems with Unknown Service Rates
In our problem, certainty equivalent control switches between an always admit policy (always explore) and a never admit policy (immediately terminate learning), which is distinct from the adaptive control literature.
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure
For the bandit setting, we propose a hierarchical bandit algorithm that achieves a near-optimal gap-independent regret of $\widetilde{\mathcal{O}}(\sqrt{ABT})$ and a near-optimal gap-dependent regret of $\mathcal{O}(\log(T))$, where $A$ and $B$ are the numbers of actions of the leader and the follower, respectively, and $T$ is the number of steps.
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning
Due to information asymmetry, finding optimal policies for Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) is hard with the complexity growing doubly exponentially in the horizon length.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Empirical Policy Evaluation with Supergraphs
We devise and analyze algorithms for the empirical policy evaluation problem in reinforcement learning.
On the role of clustering in Personalized PageRank estimation
We then show that the common underlying graph can be leveraged to efficiently and jointly estimate PPR for many pairs, rather than treating each pair separately using the primitive algorithm.
Social and Information Networks
