世界模型 - arXivDaily 专题

2010.04444 2021-08-23 cs.LG cs.AI 56%

Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning

Paul J. Pritz, Liang Ma, Kin K. Leung

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

2106.04799 2021-06-10 cs.LG 56%

Pretraining Representations for Data-Efficient Reinforcement Learning

Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, Devon Hjelm, Philip Bachman, Aaron Courville

专题命中模型式强化学习：latent dynamics(abstract)；分类 cs.LG；dynamics model(abstract)

2011.10605 2021-04-30 cs.LG cs.RO 56%

Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System

Junhyeok Ahn, Luis Sentis

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.LG、cs.RO

Comments Accepted to 2021 L4DC

2104.00163 2021-04-02 cs.LG cs.AI 56%

DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation

Faraz Torabi, Garrett Warnell, Peter Stone

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

2103.10369 2021-03-19 cs.LG cs.AI stat.ML 56%

Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning

Sebastian Curi, Ilija Bogunovic, Andreas Krause

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

2005.05951 2021-03-03 cs.LG cs.AI stat.ML 56%

MOReL : Model-Based Offline Reinforcement Learning

Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

专题命中模型式强化学习：model-based RL(abstract)；分类 cs.AI、cs.LG

Comments First two authors contributed equally. Published at NeurIPS 2020. After publication at NeurIPS 2020, (1) D4RL benchmark results have been added; (2) hyper-parameter ablation studies have been added; (3) scope of Lemma 3 has been extended

1906.09323 2021-01-29 cs.LG cs.AI cs.GT stat.ML 56%

Reinforcement Learning with Convex Constraints

Sobhan Miryoosefi, Kianté Brantley, Hal Daumé, Miroslav Dudik, Robert Schapire

专题命中模型式强化学习：model-based RL(abstract)；分类 cs.AI、cs.LG

Journal ref Advances in Neural Information Processing Systems 32 (2019), 14093-14102

2101.08740 2021-01-22 cs.RO cs.LG cs.SY eess.SY 56%

Model-based Policy Search for Partially Measurable Systems

Fabio Amadio, Alberto Dalla Libera, Ruggero Carli, Daniel Nikovski, Diego Romeres

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.LG、cs.RO

Comments Accepted to 3rd Robot Learning Workshop: Grounding Machine Learning Development in the Real World (NeurIPS 2020)

2010.15948 2021-01-06 cs.RO cs.LG 56%

Bayes-Adaptive Deep Model-Based Policy Optimisation

Tai Hoang, Ngo Anh Vien

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.LG、cs.RO

Comments Source code https://github.com/thobotics/RoMBRL

2011.06464 2020-12-29 cs.RO cs.AI cs.CV cs.LG 56%

3D-OES: Viewpoint-Invariant Object-Factorized Environment Simulators

Hsiao-Yu Fish Tung, Zhou Xian, Mihir Prabhudesai, Shamit Lal, Katerina Fragkiadaki

专题命中模型式强化学习：分类 cs.AI、cs.LG、cs.CV；dynamics model(abstract)

2010.04279 2020-12-23 cs.LG cs.AI 56%

Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies

Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David Sontag

专题命中模型式强化学习：model-based RL(abstract)；分类 cs.AI、cs.LG

Comments To appear in AMIA 2021 virtual informatics summit

2005.13239 2020-11-24 cs.LG cs.AI stat.ML 56%

MOPO: Model-based Offline Policy Optimization

Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma

专题命中模型式强化学习：model-based RL(abstract)；分类 cs.AI、cs.LG

Comments NeurIPS 2020. First two authors contributed equally. Last two authors advised equally

2011.00825 2020-11-03 cs.LG cs.AI 56%

Reinforcement Learning with Efficient Active Feature Acquisition

Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

1909.04115 2020-10-20 cs.LG cs.AI stat.ML 56%

Gradient-Aware Model-based Policy Search

Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, Marcello Restelli

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

2010.05545 2020-10-13 cs.LG cs.AI stat.ML 56%

Local Search for Policy Iteration in Continuous Control

Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin Riedmiller

专题命中模型式强化学习：model-based RL(abstract)；分类 cs.AI、cs.LG

2007.05896 2020-07-14 cs.LG cs.AI stat.ML 56%

Learning Abstract Models for Strategic Exploration and Fast Reward Transfer

Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri, Kelvin Guu, Panupong Pasupat, Emma Brunskill, Percy Liang

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

2006.04802 2020-06-30 cs.LG stat.ML 56%

Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors

Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.LG；dynamics model(abstract)

Comments ICML BIG Workshop 2020, camera ready version

2006.05443 2020-06-25 cs.LG cs.AI stat.ML 56%

Variational Model-based Policy Optimization

Yinlam Chow, Brandon Cui, MoonKyung Ryu, Mohammad Ghavamzadeh

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

2006.09919 2020-06-18 cs.LG cs.AI stat.ML 56%

Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Control

Hua Zheng, Wei Xie, Mingbin Ben Feng

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments 12 pages, 1 figures. To appear in the Proceedings of the 2020 Winter Simulation Conference (WSC)

2005.08068 2020-05-19 cs.LG cs.AI stat.ML 56%

Model-Augmented Actor-Critic: Backpropagating through Paths

Ignasi Clavera, Violet Fu, Pieter Abbeel

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments Accepted paper at ICLR 2020

1907.01657 2020-02-18 cs.LG cs.RO stat.ML 56%

Dynamics-Aware Unsupervised Discovery of Skills

Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.LG、cs.RO

2002.05822 2020-02-17 cs.LG cs.AI stat.ML 56%

Frequency-based Search-control in Dyna

Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments Accepted to ICLR 2020

1909.01506 2020-02-12 cs.LG stat.ML 56%

Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

Nir Levine, Yinlam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui

专题命中模型式强化学习：latent dynamics(abstract)；分类 cs.LG；dynamics model(abstract)

1905.01072 2020-01-27 cs.LG cs.AI stat.ML 56%

Deep Residual Reinforcement Learning

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments AAMAS 2020

1903.11981 2019-12-30 cs.LG cs.RO stat.ML 56%

Regularizing Trajectory Optimization with Denoising Autoencoders

Rinu Boney, Norman Di Palo, Mathias Berglund, Alexander Ilin, Juho Kannala, Antti Rasmus, Harri Valpola

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.LG、cs.RO

Comments NeurIPS 2019

1905.12425 2019-12-12 cs.LG cs.AI cs.GT stat.ML 56%

Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities

Aristide Tossou, Debabrota Basu, Christos Dimitrakakis

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments the algorithm has been simplified (no need to look at lower bound of the reward and transitions). Proof has been significantly clean-up. The previous "assumption" is clarified as a condition of the algorithm well-known as sub-modularity. The proof that the bounds satisfy the submodularity is clean-up

1911.10601 2019-11-26 cs.LG cs.AI cs.IT cs.SY eess.SY math.IT stat.ML 56%

Scaling active inference

Alexander Tschantz, Manuel Baltieri, Anil. K. Seth, Christopher L. Buckley

专题命中模型式强化学习：model-based RL(abstract)；分类 cs.AI、cs.LG

1909.09186 2019-09-23 cs.LG cs.AI 56%

Revisit Policy Optimization in Matrix Form

Sitao Luan, Xiao-Wen Chang, Doina Precup

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

1909.07373 2019-09-18 cs.LG cs.AI stat.ML 56%

Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space

Zac Wellmer, James Kwok

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments Published at ECML PKDD 2019

1906.07791 2019-07-05 cs.LG cs.AI stat.ML 56%

Hill Climbing on Value Estimates for Search-control in Dyna

Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments IJCAI 2019

1. 模型式强化学习 1124 篇

Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning

Pretraining Representations for Data-Efficient Reinforcement Learning

Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System

DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation

Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning

MOReL : Model-Based Offline Reinforcement Learning

Reinforcement Learning with Convex Constraints

Model-based Policy Search for Partially Measurable Systems

Bayes-Adaptive Deep Model-Based Policy Optimisation

3D-OES: Viewpoint-Invariant Object-Factorized Environment Simulators

Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies

MOPO: Model-based Offline Policy Optimization

Reinforcement Learning with Efficient Active Feature Acquisition

Gradient-Aware Model-based Policy Search

Local Search for Policy Iteration in Continuous Control

Learning Abstract Models for Strategic Exploration and Fast Reward Transfer

Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors

Variational Model-based Policy Optimization

Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Control

Model-Augmented Actor-Critic: Backpropagating through Paths

Dynamics-Aware Unsupervised Discovery of Skills

Frequency-based Search-control in Dyna

Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

Deep Residual Reinforcement Learning

Regularizing Trajectory Optimization with Denoising Autoencoders

Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities

Scaling active inference

Revisit Policy Optimization in Matrix Form

Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space

Hill Climbing on Value Estimates for Search-control in Dyna