4 accepted papers
Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment. A family of approaches to this problem assume domain knowledge in the form of a (partial) model of this environment to decide upon the s…
We study the problem of decentralized classification conducted over a network of mobile sensors. We model the multiagent classification task as a hypothesis testing problem where each sensor has to almost surely find the true hypothesis from a finite set of candidate hypotheses. Each sensor makes no…
We study planning problems where a controllable agent operates under partial observability and interacts with an uncontrollable opponent; also referred to as the adversary. The agent has two distinct objectives: To maximize an expected value and to adhere to a safety specification. Multi-objective p…
Recurrent neural networks (RNNs) have emerged as an effective representation of control policies in sequential decision-making problems. However, a major drawback in the application of RNN-based policies is the difficulty in providing formal guarantees on the satisfaction of behavioral specificatio…