275 citations · 344 across the 8 of their papers we have counts for
8 papers
Sufficiently Accurate Model Learning for Planning
Clark Zhang, Santiago Paternain, Alejandro Ribeiro
Data driven models of dynamical systems help planners and controllers to provide more precise and accurate motions. Most model learning algorithms will try to minimize a loss funct…
Sufficiently Accurate Model Learning
Clark Zhang, Arbaaz Khan, Santiago Paternain +1
Modeling how a robot interacts with the environment around it is an important prerequisite for designing control and planning algorithms. In fact, the performance of controllers an…
Learning Optimal Resource Allocations in Wireless Systems
Mark Eisen, Clark Zhang, Luiz F. O. Chamon +2
This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with st…
Learning Implicit Sampling Distributions for Motion Planning
Clark Zhang, Jinwook Huh, Daniel D. Lee
Sampling-based motion planners have experienced much success due to their ability to efficiently and evenly explore the state space. However, for many tasks, it may be more efficie…
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients
Arbaaz Khan, Clark Zhang, Daniel D. Lee +2
In this paper, we explore using deep reinforcement learning for problems with multiple agents. Most existing methods for deep multi-agent reinforcement learning consider only a sma…
Assumed Density Filtering Q-learning
Heejin Jeong, Clark Zhang, George J. Pappas +1
While off-policy temporal difference (TD) methods have widely been used in reinforcement learning due to their efficiency and simple implementation, their Bayesian counterparts hav…