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 …
[PDF][PDF] TA-Explore: Teacher-assisted exploration for facilitating fast reinforcement learning
A Beikmohammadi, S Magnússon - Proceedings of the 2023 …, 2023 - ifaamas.org
Reinforcement Learning (RL) is crucial for data-driven decisionmaking but suffers from
sample inefficiency. This poses a risk to system safety and can be costly in real-world …
sample inefficiency. This poses a risk to system safety and can be costly in real-world …
Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis
Over the last decades, there has been growing interest in research in multiple and
interdisciplinary fields of human-AI computing. In particular, approaches integrating the …
interdisciplinary fields of human-AI computing. In particular, approaches integrating the …
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 …