Policy optimization, which learns the policy of interest by maximizing the value function via
large-scale optimization techniques, lies at the heart of modern reinforcement learning (RL) …

Gynaecological cancers encompass a spectrum of malignancies affecting the female
reproductive system, comprising the cervix, uterus, ovaries, vulva, vagina, and fallopian …

Motion planning and its optimization is vital and difficult for redundant robot manipulator in
an environment with obstacles. In this article, a general motion planning framework that …

This work provides a complete framework for the simulation, co-optimization, and sim-to-real
transfer of the design and control of soft legged robots. The compliance of soft robots …

Many policy optimization approaches in reinforcement learning incorporate a Kullback-
Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too …

To date, commercial fully autonomous driving is not realized, while level 3 is the next step in
the development of autonomous driving. At level 3, the vehicle is driving under the control of …

Explicit exploration in the action space was assumed to be indispensable for online policy
gradient methods to avoid a drastic degradation in sample complexity, for solving general …

Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards Page 1
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards Kyungjae Lee …

Tsallis and Rényi entropies, which are monotone transformations of each other, are
deformations of the celebrated Shannon entropy. Maximization of these deformed entropies …

This work provides a complete framework for the simulation, co-optimization, and sim-to-real
transfer of the design and control of soft legged robots. Soft robots have “mechanical …