We consider concurrent games played on graphs. At every round of the game, each player simultaneously and independently selects a move; the moves jointly determine the transition ...
Krishnendu Chatterjee, Luca de Alfaro, Thomas A. H...
Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
Abstract-In this paper, a Q-learning-based hybrid automatic repeat request (Q-HARQ) scheme is proposed to achieve efficient resource utilization for high speed downlink packet acc...
Recent scaling up of decentralized partially observable Markov decision process (DEC-POMDP) solvers towards realistic applications is mainly due to approximate methods. Of this fa...
Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute acti...