Compressed Federated Reinforcement Learning with a Generative Model
Reinforcement learning has recently gained unprecedented popularity, yet it still grapples
with sample inefficiency. Addressing this challenge, federated reinforcement learning …
with sample inefficiency. Addressing this challenge, federated reinforcement learning …
[HTML][HTML] Accelerating actor-critic-based algorithms via pseudo-labels derived from prior knowledge
A Beikmohammadi, S Magnússon - Information Sciences, 2024 - Elsevier
Despite the huge success of reinforcement learning (RL) in solving many difficult problems,
its Achilles heel has always been sample inefficiency. On the other hand, in RL, taking …
its Achilles heel has always been sample inefficiency. On the other hand, in RL, taking …
Multi-Objective and Constrained Reinforcement Learning for IoT
S Vaishnav, S Magnússon - Learning Techniques for the Internet of Things, 2023 - Springer
IoT networks of the future will be characterized by autonomous decision-making by
individual devices. Decision-making is done with the purpose of optimizing certain …
individual devices. Decision-making is done with the purpose of optimizing certain …
Learning to Communicate through Multi-Agent Reinforcement Learning (MARL): A Systematic Literature Review
A Beikmohammadi - 2024 - preprints.org
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered remarkable success in solving various sequential decision-making problems …
has registered remarkable success in solving various sequential decision-making problems …