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Found 19 papers, 1 papers with code
Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation
However, in many practical settings with highly scarce resources, where each agent can only receive at most one resource, such as healthcare intervention programs, the standard RMAB framework falls short.
INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent
Despite this progress, the field currently encounters two main challenges: (1) the lack of a comprehensive LLM agent framework adaptable to a variety of financial tasks, and (2) the absence of standardized benchmarks and consistent datasets for assessing agent performance.
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
In this paper, we take a further step and introduce a \emph{personalized} FedRL framework (PFedRL) by taking advantage of possibly shared common structure among agents in heterogeneous environments.
Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach
Maternal mortality remains a significant global public health challenge.
DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback
In this paper, we introduce Pref-RMAB, a new RMAB model in the presence of preference signals, where the decision maker only observes pairwise preference feedback rather than scalar reward from the activated arms at each decision epoch.
Decentralized Federated Learning with Model Caching on Mobile Agents
Decentralized FL (DFL) utilizes local model exchange and aggregation between agents to reduce the communication and computation overheads on the central server.
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
We begin with FinLLaMA, pre-trained on a 52 billion token financial corpus, incorporating text, tables, and time-series data to embed comprehensive financial knowledge.
FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs.
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
Restless multi-armed bandits (RMAB) play a central role in modeling sequential decision making problems under an instantaneous activation constraint that at most B arms can be activated at any decision epoch.
Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks
We study the data packet transmission problem (mmDPT) in dense cell-free millimeter wave (mmWave) networks, i. e., users sending data packet requests to access points (APs) via uplinks and APs transmitting requested data packets to users via downlinks.
FinBen: A Holistic Financial Benchmark for Large Language Models
Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting.
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations
Decentralized learning has emerged as an alternative method to the popular parameter-server framework which suffers from high communication burden, single-point failure and scalability issues due to the need of a central server.
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints
Restless multi-armed bandits (RMAB) have been widely used to model sequential decision making problems with constraints.
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular parameter server framework.
Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits
Most research for this problem focuses exclusively on the settings that players have \textit{full access} to all arms and receive no reward when pulling the same arm.
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation
Using this structural result, we establish the indexability of our problem, and employ the Whittle index policy to minimize average latency.
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits
We call it the R(MA)^2B-UCB algorithm.
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
In this paradigm, each worker maintains a local estimate of the optimal parameter vector, and iteratively updates it by waiting and averaging all estimates obtained from its neighbors, and then corrects it on the basis of its local dataset.
Learning Augmented Index Policy for Optimal Service Placement at the Network Edge
We pose the problem as a Markov decision process (MDP) in which the system state is given by describing, for each service, the number of customers that are currently waiting at the edge to obtain the service.
