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Found 7 papers, 0 papers with code
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks
We propose a novel multi-agent reinforcement learning (RL) framework for distributed DCA, named Channel Allocation RL To Overlapped Networks (CARLTON).
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation
To handle statistical heterogeneity of users data, which is a second major challenge in FL, we extend BAAF to allow for appropriate local updates by the users and develop the Controlled Bayesian Air Aggregation Federated-learning (COBAAF) algorithm.
Restless Multi-Armed Bandits under Exogenous Global Markov Process
Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time t the arm that maximizes the expected immediate value.
Learning in Restless Bandits under Exogenous Global Markov Process
Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time $t$ the arm that maximizes the expected immediate value.
Federated Learning: A Signal Processing Perspective
Learning in a federated manner differs from conventional centralized machine learning, and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications.
Distributed Learning over Markovian Fading Channels for Stable Spectrum Access
By contrast, we consider a more general and practical model, where each channel yields a different expected rate for each user.
Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules
Although existing methods have shown a logarithmic regret order with time in this RMAB setting, the theoretical analysis shows a significant improvement in the regret scaling with respect to the system parameters under ASR.
