Beyond high performance, the RL framework’s main advantage lies in its real-time application potential. Once trained, the ...

In this course we introduce the fundamentals of Deep Reinforcement Learning from scratch starting from its roots in Dynamic Programming and optimal control, and ending with some of the most popular ...

We demonstrate the design of low entropy state schemes that increase coherent control in quantum evolution through laser ... the structural noise of laser by evolving the system with an optimal ...

A number of other applications in healthcare have included resource scheduling and allocation, optimal process control and drug discovery. Deep-reinforcement learning is also being applied to help ...

A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this ...

deep reinforcement learning will have an increasing impact on industries. It will lead to quantum improvements in robotic automation and system control on the same order as we saw with Go.

Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning. Journal of NeuroEngineering and Rehabilitation , 2023; 20 (1 ...

reducing manual intervention and enhancing precision in real-time control. By integrating virtual accelerators and reinforcement learning, the system achieves seamless transition from simulation ...