Simplifying Reward Design through Divide-and-Conquer

7 June 2018

Papers citing "Simplifying Reward Design through Divide-and-Conquer"

10 / 10 papers shown

Title
Direct Behavior Specification via Constrained Reinforcement Learning Julien Roy Roger Girgis Joshua Romoff Pierre-Luc Bacon C. Pal 114 36 0 22 Dec 2021
Assisted Robust Reward Design Jerry Zhi-Yang He Anca Dragan 54 9 0 18 Nov 2021
What are you optimizing for? Aligning Recommender Systems with Human Values J. Stray Ivan Vendrov Jeremy Nixon Steven Adler Dylan Hadfield-Menell OffRL 55 55 0 22 Jul 2021
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning Yuexiang Zhai Christina Baek Zhengyuan Zhou Jiantao Jiao Yi-An Ma 85 23 0 08 Jul 2021
Policy Gradient Bayesian Robust Optimization for Imitation Learning Zaynah Javed Daniel S. Brown Satvik Sharma Jerry Zhu Ashwin Balakrishna Marek Petrik Anca Dragan Ken Goldberg 126 16 0 11 Jun 2021
Efficient learning of goal-oriented push-grasping synergy in clutter Kechun Xu Hongxiang Yu Qianen Lai Yue Wang R. Xiong 86 69 0 09 Mar 2021
Reward-rational (implicit) choice: A unifying formalism for reward learning Hong Jun Jeon S. Milli Anca Dragan 98 177 0 12 Feb 2020
Learning Reward Functions by Integrating Human Demonstrations and Preferences Malayandi Palan Nicholas C. Landolfi Gleb Shevchuk Dorsa Sadigh 64 126 0 21 Jun 2019
Planning With Uncertain Specifications (PUnS) Ankit J. Shah Shen Li J. Shah 78 25 0 07 Jun 2019
M $^3$ RL: Mind-aware Multi-agent Management Reinforcement Learning Tianmin Shu Yuandong Tian 92 54 0 29 Sep 2018