An overview of the action space for deep reinforcement learning
J Zhu, F Wu, J Zhao - Proceedings of the 2021 4th International …, 2021 - dl.acm.org
In recent years, deep reinforcement learning has been applied to tasks in the real world
gradually. Especially in the field of control, reinforcement learning has shown …
gradually. Especially in the field of control, reinforcement learning has shown …
A survey on physics informed reinforcement learning: Review and open problems
C Banerjee, K Nguyen, C Fookes, M Raissi - ar** air into the lungs, which is a life-
saving supportive therapy in an intensive care unit (ICU). In clinical scenarios, each patient …
saving supportive therapy in an intensive care unit (ICU). In clinical scenarios, each patient …
Smooth exploration for robotic reinforcement learning
Reinforcement learning (RL) enables robots to learn skills from interactions with the real
world. In practice, the unstructured step-based exploration used in Deep RL–often very …
world. In practice, the unstructured step-based exploration used in Deep RL–often very …
Learning insertion primitives with discrete-continuous hybrid action space for robotic assembly tasks
This paper introduces a discrete-continuous action space to learn insertion primitives for
robotic assembly tasks. Primitives are sequences of elementary actions with certain exit …
robotic assembly tasks. Primitives are sequences of elementary actions with certain exit …
Action decoupled SAC reinforcement learning with discrete-continuous hybrid action spaces
Y Xu, Y Wei, K Jiang, L Chen, D Wang, H Deng - Neurocomputing, 2023 - Elsevier
Abstract Most existing Deep Reinforcement Learning (DRL) algorithms solely apply to
discrete action or continuous action spaces. However, the agent often has both continuous …
discrete action or continuous action spaces. However, the agent often has both continuous …