Multi-task learning is an important problem in reinforcement learning. Training multiple tasks
together brings benefits from the shared useful information across different tasks and often …

Aiming at the difficulty of dynamic modeling of a hybrid robotic arm, a dynamic model system
of industrial robotic arm based on Simscape Multibody was established with the MG400 …

Multi-task learning has expanded the boundaries of robotic manipulation, enabling the
execution of increasingly complex tasks. However, policies learned through reinforcement …

Multi-task learning is important in reinforcement learning where simultaneously training
across different tasks allows for leveraging shared information among them, typically leading …

On-policy reinforcement learning (RL) has become a popular framework for solving
sequential decision problems due to its computational efficiency and theoretical simplicity …

The past few years have seen an explosion of interest in using machine learning to make
robots capable of learning a diverse set of tasks. These robots use Reinforcement Learning …