Composing Task-Agnostic Policies with Deep Reinforcement Learning

Composing Task-Agnostic Policies with Deep Reinforcement Learning

25 May 2019

Jacob J. Johnson

Taylor Henderson

Papers citing "Composing Task-Agnostic Policies with Deep Reinforcement Learning"

14 / 14 papers shown

Title
Skill Expansion and Composition in Parameter Space Tenglong Liu J. Li Yinan Zheng Haoyi Niu Yixing Lan Xin Xu Xianyuan Zhan 58 4 0 09 Feb 2025
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction Seohong Park Oleh Rybkin Sergey Levine OffRL 33 34 0 13 Oct 2023
On the Value of Myopic Behavior in Policy Reuse Kang Xu Chenjia Bai Shuang Qiu Haoran He Bin Zhao Zhen Wang Wei Li Xuelong Li 24 1 0 28 May 2023
A Constrained-Optimization Approach to the Execution of Prioritized Stacks of Learned Multi-Robot Tasks Gennaro Notomista 17 2 0 13 Jan 2023
Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling Daniel Lawson A. H. Qureshi 22 7 0 11 Nov 2022
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning Zih-Yun Chiu Yi-Lin Tuan William Yang Wang Michael C. Yip OffRL 25 3 0 07 Oct 2022
Meta-Learning Parameterized Skills Haotian Fu Shangqun Yu Saket Tiwari Michael Littman G. Konidaris 27 6 0 07 Jun 2022
Hierarchical Skills for Efficient Exploration Jonas Gehring Gabriel Synnaeve Andreas Krause Nicolas Usunier 26 40 0 20 Oct 2021
Hierarchical Primitive Composition: Simultaneous Activation of Skills with Inconsistent Action Dimensions in Multiple Hierarchies Jeong-Hoon Lee Jongeun Choi 21 8 0 05 Oct 2021
Learning Multimodal Rewards from Rankings Vivek Myers Erdem Biyik Nima Anari Dorsa Sadigh OffRL 24 48 0 27 Sep 2021
Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving Zhangjie Cao Erdem Biyik Woodrow Z. Wang Allan Raventos Adrien Gaidon Guy Rosman Dorsa Sadigh 26 65 0 01 Jul 2020
Multi-Task Reinforcement Learning with Soft Modularization Ruihan Yang Huazhe Xu Yi Wu Xiaolong Wang 27 174 0 30 Mar 2020
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners A. H. Qureshi Yinglong Miao Anthony Simeonov Michael C. Yip PINN 3DV 28 212 0 13 Jul 2019
Building machines that adapt and compute like brains Brenden Lake J. Tenenbaum AI4CE FedML NAI AILaw 254 890 0 11 Nov 2017