7 accepted papers
Despite remarkable achievements in artificial intelligence, the deployability of learning-enabled systems in high-stakes real-world environments still faces persistent challenges. For example, in safety-critical domains like autonomous driving, robotic manipulation, and healthcare, it is crucial not…
Safety has been a critical issue for the deployment of learning-based approaches in real-world applications. To address this issue, control barrier function (CBF) and its variants have attracted extensive attention for safety-critical control. However, due to the myopic one-step nature of CBF and th
The main challenge of multiagent reinforcement learning is the difficulty of learning useful policies in the presence of other simultaneously learning agents whose changing behaviors jointly affect the environment's transition and reward dynamics. An effective approach that has recently emerged for…
In a multirobot system, a number of cyber-physical attacks (e.g., communication hijack, observation per-turbations) can challenge the robustness of agents. This robust-ness issue worsens in multiagent reinforcement learning because there exists the non-stationarity of the environment caused by simul…
A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents. In particular, each agent perceives the environment as effectively non-stationary due to the changing policies of other agents. Moreover, e…
This paper investigates reinforcement learning with constraints, which are indispensable in safety-critical environments. To drive the constraint violation to decrease monotonically, we take the constraints as Lyapunov functions and impose new linear constraints on the policy parameters’ updating dy…
The complexity of multiagent reinforcement learning (MARL) in multiagent systems increases exponentially with respect to the agent number. This scalability issue prevents MARL from being applied in large-scale multiagent systems. However, one critical feature in MARL that is often neglected is that…