CRMRL: Collaborative Relationship Meta Reinforcement Learning for Effectively Adapting to Type Changes in Multi-Robotic System
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3196782
Abstract: Multi-agent reinforcement learning methods have been widely used for multi-robotic systems, and meta-learning methods are also applied to help robots reuse prior experiences to guide new tasks learning. But in some multi-robotic tasks, the robot… read more here.
Keywords: multi robotic; reinforcement learning; reinforcement; relationship ... See more keywords
Meta Reinforcement Learning for Optimal Design of Legged Robots
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3211785
Abstract: The process of robot design is a complex task and the majority of design decisions are still based on human intuition or tedious manual tuning. A more informed way of facing this task is computational… read more here.
Keywords: reinforcement learning; learning optimal; meta reinforcement; design ... See more keywords
Meta-Reinforcement Learning in Nonstationary and Nonparametric Environments.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3270298
Abstract: Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills. Meta-reinforcement learning (meta-RL) addresses… read more here.
Keywords: learning nonstationary; reinforcement learning; task; meta reinforcement ... See more keywords
Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3185549
Abstract: In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms… read more here.
Keywords: non stationary; reinforcement learning; stationary environments; task ... See more keywords