Specifically, we develop and utilize the multi-agent multi-armed bandit (MAB) problem to model and study how multiple interacting agents make decisions that balance the explore-exploit tradeoff. we consider several different communication protocols for sharing information between agents. We develop and ...
Then a decentralized multi-agent multi-armed bandit (MAMAB) algorithm is developed for each SBS to decide its own cache strategy based jointly on its past observations and estimated upcoming cache action of other SBSs. This decentralized MAMAB algorithm with $\epsilon $ -calibration enables ...
n‐armed bandit problempolicy iteration algorithmreinforcement learningmulti agent machine learning a reinforcement approach multi-agent machine learning: a reinforcement approach by multi agent machine learning a reinforcement approach multi agent machine learning a reinforcement approach user crowd simulation ...
Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards We study a decentralized multi-agent multi-armed bandit problem in which multiple clients are connected by time dependent random graphs provided by an envi... M Xu,D Klabjan - 《Arxiv》 被引量: 0发表: ...
In a multi-armed bandit problem, the reward of each action (arm) follows a normal distribution, whose characteristic parameters are unknown to the agent... D.,Fern,src=http://onlinelibrarystatic.wiley.com/undisplayable_characters/0000e0.gif,... - 《Water Resources Research》 被引量: 163...
3.1. Multi-agent reinforcement learning problem According to problems P1 and P2, VUEs need to decide which V2I links can be multiplexed and how much power should be allocated to optimize the data delay performance. Hence, each VUE can be regarded as an agent that interacts with the unknown ...
This paper addresses the problem of ad hoc teamwork, where a learning agent engages in a cooperative task with other (unknown) agents. The agent must effec
We found a problem during the transitions between matches, which did not happen during our local tests. In addition, we noticed some issues when agents needed to submit tasks of two blocks and defend task boards and goal zones to prevent the opponent agents to complete their tasks. ...
We study a distributed decision-making problem in which multiple agents face the same multi-armed bandit (MAB), and each agent makes sequential choices among arms to maximize its own individual reward. The agents cooperate by sharing their estimates over a fixed communication graph. We consider ...
Multi-agent path rindingNon-asymptotic performanceStochastic processMulti-armed bandit problemMulti-agent path finding (MAPF) is a classical NP-hard problem that considers planning collision-free paths for multiple agents simultaneously. A MAPF problem is typically solved via addressing a sequence of ...