On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
This paper presents a study of robust policy networks in deep reinforcement learning. We
investigate the benefits of policy parameterizations that naturally satisfy constraints on their …
investigate the benefits of policy parameterizations that naturally satisfy constraints on their …
FMD-IoV: Security and Robust Enhancement for Federated Multi-Domain Learning–Based IoV
The rapid development of intelligent transportation and autonomous driving technologies,
driven by the Internet of Vehicles (IoV), faces significant challenges owing to data and …
driven by the Internet of Vehicles (IoV), faces significant challenges owing to data and …
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Establishing robust policies is essential to counter attacks or disturbances affecting deep
reinforcement learning (DRL) agents. Recent studies explore state-adversarial robustness …
reinforcement learning (DRL) agents. Recent studies explore state-adversarial robustness …
Efficient Compensation of Action for Reinforcement Learning Policies in Sim2Real
W Zhang, S Xie, X Luo, W Xiao, T Wang - 2024 IEEE 36th …, 2024 - computer.org
Simulation to reality (sim-to-real) transfer is a promising alternative for training behavioral
policies in reinforcement learning (RL). However, many significant differences between the …
policies in reinforcement learning (RL). However, many significant differences between the …