机器人 / 具身智能 - arXivDaily 专题

1901.03737 2019-07-22 cs.RO cs.LG 62%

Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning

Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Roberto Calandra, Sergey Levine, Kristofer S. J. Pister

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG

Comments Accepted to IROS and RA-L, 2019. For more information, see the website: https://sites.google.com/berkeley.edu/mbrl-quadrotor/. 9 pages, 12 figures

1905.07866 2019-07-16 cs.RO cs.LG 62%

Reinforcement Learning without Ground-Truth State

Xingyu Lin, Harjatin Singh Baweja, David Held

专题命中模仿学习与强化学习：manipulation(abstract)；分类 cs.RO、cs.LG

1907.05855 2019-07-15 cs.LG cs.AI stat.ML 62%

DisCoRL: Continual Reinforcement Learning via Policy Distillation

René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Guanghang Cai, Natalia Díaz-Rodríguez, David Filliat

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

Comments arXiv admin note: text overlap with arXiv:1906.04452

1811.02184 2019-07-10 cs.RO cs.LG 62%

Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning

Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.RO、cs.LG

1906.08928 2019-06-24 cs.RO cs.AI 62%

Learning Reward Functions by Integrating Human Demonstrations and Preferences

Malayandi Palan, Nicholas C. Landolfi, Gleb Shevchuk, Dorsa Sadigh

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.RO、cs.AI

Comments Presented at RSS 2019

1906.05329 2019-06-14 cs.LG cs.AI stat.ML 62%

Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization

Tom Jurgenson, Edward Groshev, Aviv Tamar

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

Comments 15 pages (8 main), 2 figures, 4 tables

1906.04452 2019-06-12 cs.LG cs.RO stat.ML 62%

Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer

René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Natalia Díaz-Rodríguez, David Filliat

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.RO、cs.LG

Comments accepted to the Workshop on Multi-Task and Lifelong Reinforcement Learning, ICML 2019

1712.06924 2019-06-11 cs.LG cs.AI stat.ML 62%

Safe Policy Improvement with Baseline Bootstrapping

Romain Laroche, Paul Trichelair, Rémi Tachet des Combes

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

Comments accepted as a long oral at ICML2019

1906.00214 2019-06-04 cs.RO cs.LG stat.ML 62%

Harnessing Reinforcement Learning for Neural Motion Planning

Tom Jurgenson, Aviv Tamar

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG

Comments 13 pages (all), 8 pages (main sections), 6 figures, 4 tables, accepted to rss2019

1905.07028 2019-05-20 cs.AI cs.RO cs.SC 62%

A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains

Laszlo Treszkai, Vaishak Belle

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.RO、cs.AI

1905.05731 2019-05-15 cs.LG cs.AI stat.ML 62%

Successor Options: An Option Discovery Framework for Reinforcement Learning

Rahul Ramesh, Manan Tomar, Balaraman Ravindran

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.AI、cs.LG

Comments To appear in the proceedings of the International Joint Conference on Artificial Intelligence 2019 (IJCAI)

1903.01567 2019-03-06 cs.LG cs.AI cs.NE 62%

Model Primitive Hierarchical Lifelong Reinforcement Learning

Bohan Wu, Jayesh K. Gupta, Mykel J. Kochenderfer

专题命中模仿学习与强化学习：world model(abstract)；分类 cs.AI、cs.LG

Comments 9 pages, 10 figures. Accepted as a full paper at AAMAS 2019

Journal ref International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019)

1902.07015 2019-02-21 cs.LG cs.AI stat.ML 62%

Investigating Generalisation in Continuous Deep Reinforcement Learning

Chenyang Zhao, Olivier Sigaud, Freek Stulp, Timothy M. Hospedales

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

1805.04686 2019-02-19 cs.LG cs.RO stat.ML 62%

Task Transfer by Preference-Based Cost Learning

Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG

Comments Accepted to AAAI 2019. Mingxuan Jing and Xiaojian Ma contributed equally to this work

1902.04546 2019-02-13 cs.LG cs.AI cs.NE stat.ML 62%

ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning

Harris Chan, Yuhuai Wu, Jamie Kiros, Sanja Fidler, Jimmy Ba

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

1901.10031 2019-02-13 cs.LG cs.AI stat.ML 62%

Lyapunov-based Safe Policy Optimization for Continuous Control

Yinlam Chow, Ofir Nachum, Aleksandra Faust, Edgar Duenez-Guzman, Mohammad Ghavamzadeh

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

1902.01554 2019-02-06 cs.AI cs.LG cs.MA 62%

Learning to Schedule Communication in Multi-agent Reinforcement Learning

Daewoo Kim, Sangwoo Moon, David Hostallero, Wan Ju Kang, Taeyoung Lee, Kyunghwan Son, Yung Yi

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

Comments Accepted in ICLR 2019

1901.01994 2019-01-21 cs.LG cs.AI stat.ML 62%

Recurrent Control Nets for Deep Reinforcement Learning

Vincent Liu, Ademi Adeniji, Nathaniel Lee, Jason Zhao, Mario Srouji

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

1812.09968 2018-12-27 cs.LG cs.AI cs.NE 62%

VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control

Xingxing Liang, Qi Wang, Yanghe Feng, Zhong Liu, Jincai Huang

专题命中模仿学习与强化学习：world model(abstract)；分类 cs.AI、cs.LG

1812.00025 2018-12-04 cs.LG cs.AI 62%

Modulated Policy Hierarchies

Alexander Pashevich, Danijar Hafner, James Davidson, Rahul Sukthankar, Cordelia Schmid

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

Comments 8 pages, 5 figures

1811.12560 2018-12-04 cs.LG cs.AI stat.ML 62%

An Introduction to Deep Reinforcement Learning

Vincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

Journal ref Foundations and Trends in Machine Learning: Vol. 11, No. 3-4, 2018

1710.11424 2018-10-26 cs.LG cs.AI 62%

Regret Minimization for Partially Observable Deep Reinforcement Learning

Peter Jin, Kurt Keutzer, Sergey Levine

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

Comments ICML 2018

1802.06070 2018-10-11 cs.AI cs.RO 62%

Diversity is All You Need: Learning Skills without a Reward Function

Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.AI

Comments Videos and code for our experiments are available at: https://sites.google.com/view/diayn

1805.08296 2018-10-08 cs.LG cs.AI stat.ML 62%

Data-Efficient Hierarchical Reinforcement Learning

Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.AI、cs.LG

Comments NIPS 2018

1810.00361 2018-10-02 cs.LG cs.AI stat.ML 62%

Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning

Gino Brunner, Manuel Fritsche, Oliver Richter, Roger Wattenhofer

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

1809.06064 2018-09-18 cs.LG cs.CV stat.ML 62%

Object-sensitive Deep Reinforcement Learning

Yuezhang Li, Katia Sycara, Rahul Iyer

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.CV、cs.LG

Comments 15 pages, 6 figures, Accepted at 3rd Global Conference on Artificial Intelligence (GCAI-17), Miami, 2017

1803.02291 2018-08-02 cs.RO cs.AI 62%

Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning

Juan Camilo Gamboa Higuera, David Meger, Gregory Dudek

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.RO、cs.AI

Comments 8 pages, 7 figures

1807.09205 2018-07-25 cs.RO cs.AI 62%

End-to-End Deep Imitation Learning: Robot Soccer Case Study

Okan Aşık, Binnur Görer, H. Levent Akın

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.RO、cs.AI

Comments RoboCup 2018 Symposium

1807.07530 2018-07-20 cs.LG cs.AI stat.ML 62%

Self-Organizing Maps as a Storage and Transfer Mechanism in Reinforcement Learning

Thommen George Karimpanal, Roland Bouffanais

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

Comments 7 pages, 7 figures, presented at ALA Workshop, FAIM, Stockholm, 2018

1805.08882 2018-07-17 cs.LG cs.AI stat.ML 62%

Multi-task Maximum Entropy Inverse Reinforcement Learning

Adam Gleave, Oliver Habryka

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

Comments Presented at 1st Workshop on Goal Specifications for Reinforcement Learning (ICML/IJCAI/AAMAS 2018)

1. 模仿学习与强化学习 4134 篇

Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning

Reinforcement Learning without Ground-Truth State

DisCoRL: Continual Reinforcement Learning via Policy Distillation

Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning

Learning Reward Functions by Integrating Human Demonstrations and Preferences

Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization

Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer

Safe Policy Improvement with Baseline Bootstrapping

Harnessing Reinforcement Learning for Neural Motion Planning

A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains

Successor Options: An Option Discovery Framework for Reinforcement Learning

Model Primitive Hierarchical Lifelong Reinforcement Learning

Investigating Generalisation in Continuous Deep Reinforcement Learning

Task Transfer by Preference-Based Cost Learning

ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning

Lyapunov-based Safe Policy Optimization for Continuous Control

Learning to Schedule Communication in Multi-agent Reinforcement Learning

Recurrent Control Nets for Deep Reinforcement Learning

VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control

Modulated Policy Hierarchies

An Introduction to Deep Reinforcement Learning

Regret Minimization for Partially Observable Deep Reinforcement Learning

Diversity is All You Need: Learning Skills without a Reward Function

Data-Efficient Hierarchical Reinforcement Learning

Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning

Object-sensitive Deep Reinforcement Learning

Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning

End-to-End Deep Imitation Learning: Robot Soccer Case Study

Self-Organizing Maps as a Storage and Transfer Mechanism in Reinforcement Learning

Multi-task Maximum Entropy Inverse Reinforcement Learning