22 citations · 97 across the 38 of their papers we have counts for
14 papers · 1 filter
Difference Rewards Policy Gradients
Jacopo Castellini, Sam Devlin, Frans A. Oliehoek +1
Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of…
Analog Circuit Design with Dyna-Style Reinforcement Learning
Wook Lee, Frans A. Oliehoek
In this work, we present a learning based approach to analog circuit design, where the goal is to optimize circuit performance subject to certain design constraints. One of the asp…
Loss Bounds for Approximate Influence-Based Abstraction
Elena Congeduti, Alexander Mey, Frans A. Oliehoek
Sequential decision making techniques hold great promise to improve the performance of many real-world systems, but computational complexity hampers their principled application. I…
Multi-agent active perception with prediction rewards
Mikko Lauri, Frans A. Oliehoek
Multi-agent active perception is a task where a team of agents cooperatively gathers observations to compute a joint estimate of a hidden variable. The task is decentralized and th…
Influence-Augmented Online Planning for Complex Environments
Jinke He, Miguel Suau, Frans A. Oliehoek
How can we plan efficiently in real time to control an agent in a complex environment that may involve many other agents? While existing sample-based planners have enjoyed empirica…
Real-Time Resource Allocation for Tracking Systems
Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek +1
Automated tracking is key to many computer vision applications. However, many tracking systems struggle to perform in real-time due to the high computational cost of detecting peop…