Search Results for author:
Found 9 papers, 2 papers with code
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
In this paper, we consider the problem of sequential change-point detection where both the change-points and the distributions before and after the change are assumed to be unknown.
SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning
In this paper, we perform a non-asymptotic analysis of the federated linear stochastic approximation (FedLSA) algorithm.
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits
In a typical stochastic multi-armed bandit problem, the objective is often to maximize the expected sum of rewards over some time horizon $T$.
Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study
In today's era, autonomous vehicles demand a safety level on par with aircraft.
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks
In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption.
One-Step Distributional Reinforcement Learning
Reinforcement learning (RL) allows an agent interacting sequentially with an environment to maximize its long-term expected return.
Distributional Reinforcement Learning
reinforcement-learning
+1
Regularization of the policy updates for stabilizing Mean Field Games
This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual returns.
Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes
We consider the problem of learning in a non-stationary reinforcement learning (RL) environment, where the setting can be fully described by a piecewise stationary discrete-time Markov decision process (MDP).
pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research
Microgrids, self contained electrical grids that are capable of disconnecting from the main grid, hold potential in both tackling climate change mitigation via reducing CO2 emissions and adaptation by increasing infrastructure resiliency.
