3 accepted papers
Min-max problems are important in multi-agent sequential decision-making because they improve the performance of the worst-performing agent in the network. However, solving the multi-agent min-max problem is challenging. We propose a modular, distributed, online planning-based algorithm that is able
We present Learned Risk Metric Maps (LRMM) for real-time estimation of coherent risk metrics of high-dimensional dynamical systems operating in unstructured, partially observed environments. LRMM models are simple to design and train-requiring only procedural generation of obstacle sets, state and c…
We learn interpretable end-to-end controllers based on Neural Circuit Policies (NCPs) to enable goal reaching and dynamic obstacle avoidance in flight domains. In addition to being able to learn high-quality control, NCP networks are designed with a small number of neurons. This property allows for