世界模型

A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings

Safa Alver, Doina Precup

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments Accepted to EWRL 2024

Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planning

Bradley Burega, John D. Martin, Luke Kapeluck, Michael Bowling

专题命中 仿真与规划 ：model-based RL(abstract)；分类 cs.AI、cs.LG

A New View on Planning in Online Reinforcement Learning

Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments Published in the Planning and Reinforcement Learning Workshop at ICAPS 2024. arXiv admin note: text overlap with arXiv:2206.02902

An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions

Leon Eisemann, Mirjam Fehling-Kaschek, Henrik Gommel, David Hermann, Marvin Klemp, Martin Lauer, Benjamin Lickert, Florian Luettner, Robin Moss, Nicole Neis, Maria Pohle, Simon Romanski, Daniel Stadler, Alexander Stolz, Jens Ziehn, Jingxing Zhou

专题命中 仿真与规划 ：分类 cs.AI、cs.LG、cs.CV；simulation model(abstract)

Comments 8 pages, 5 figures

Goal-Space Planning with Subgoal Models

Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott Jordan, Adam White, Gabor Mihucz, Farzane Aminmansour, Martha White

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Video Language Planning

Yilun Du, Mengjiao Yang, Pete Florence, Fei Xia, Ayzaan Wahid, Brian Ichter, Pierre Sermanet, Tianhe Yu, Pieter Abbeel, Joshua B. Tenenbaum, Leslie Kaelbling, Andy Zeng, Jonathan Tompson

专题命中 仿真与规划 ：分类 cs.AI、cs.LG、cs.CV；dynamics model(abstract)

Comments https://video-language-planning.github.io/

HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing

Luke Burks, Hunter M. Ray, Jamison McGinley, Sousheel Vunnam, Nisar Ahmed

专题命中 仿真与规划 ：environment model(abstract)；分类 cs.AI、cs.RO

Comments Accepted to IEEE Transactions on Robotics. 20 pages, 18 figures

Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving

Marvin Klimke, Benjamin Völz, Michael Buchholz

专题命中 仿真与规划 ：environment model(abstract)；分类 cs.LG、cs.RO

Comments 8 pages, 10 figures, to be published in 34th IEEE Intelligent Vehicles Symposium (IV)

Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning

Safa Alver, Doina Precup

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments Published as a conference paper at CoLLAs 2023

Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision

Faris Janjoš, Max Keller, Maxim Dolgov, J. Marius Zöllner

专题命中 仿真与规划 ：environment model(abstract)；分类 cs.AI、cs.RO

Comments 8 pages, 6 figures, to be published in 34th IEEE Intelligent Vehicles Symposium (IV)

Learning to Operate in Open Worlds by Adapting Planning Models

Wiktor Piotrowski, Roni Stern, Yoni Sher, Jacob Le, Matthew Klenk, Johan deKleer, Shiwali Mohan

专题命中 仿真与规划 ：environment model(abstract)；分类 cs.AI、cs.LG

Comments To appears in the Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)

Safe Reinforcement Learning Using Black-Box Reachability Analysis

Mahmoud Selim, Amr Alanwar, Shreyas Kousik, Grace Gao, Marco Pavone, Karl H. Johansson

专题命中 仿真与规划 ：environment model(abstract)；分类 cs.LG、cs.RO

Comments This paper is accepted at IEEE Robotics and Automation Letters and International Conference on Robotics and Automation (ICRA)

Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction

Masha Itkina, Mykel J. Kochenderfer

专题命中 仿真与规划 ：分类 cs.LG、cs.CV、cs.RO；predictive model(abstract)；predictive models(abstract)

Comments Conference on Robot Learning (CoRL) 2022, 15 pages, 4 figures

Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models

Jose L. Vazquez, Alexander Liniger, Wilko Schwarting, Daniela Rus, Luc Van Gool

专题命中 仿真与规划 ：分类 cs.AI、cs.RO、cs.MA；dynamics model(abstract)；predictive model(abstract)

Comments accepted to L4DC

Assessing Policy, Loss and Planning Combinations in Reinforcement Learning using a New Modular Architecture

Tiago Gaspar Oliveira, Arlindo L. Oliveira

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Temporal Predictive Coding For Model-Based Planning In Latent Space

Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments International Conference on Machine Learning

Vector Quantized Models for Planning

Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals

专题命中 仿真与规划 ：model-based RL(abstract)；分类 cs.AI、cs.LG

Comments ICML 2021

OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots

Bogdan Trasnea, Cosmin Ginerica, Mihai Zaha, Gigel Macesanu, Claudiu Pozna, Sorin Grigorescu

专题命中 仿真与规划 ：environment model(abstract)；分类 cs.AI、cs.RO

Journal ref Sensors 2021, 21(11), 3606

On the role of planning in model-based deep reinforcement learning

Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Veličković, Théophane Weber

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments Published at ICLR 2021

Selective Dyna-style Planning Under Limited Model Capacity

Zaheer Abbas, Samuel Sokota, Erin J. Talvitie, Martha White

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments Accepted at ICML 2020

Model-Based Visual Planning with Self-Supervised Functional Distances

Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

专题命中 仿真与规划 ：分类 cs.AI、cs.LG、cs.CV；dynamics model(abstract)

Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning

Jiajun Fan, He Ba, Xian Guo, Jianye Hao

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.LG、cs.RO

Sample-efficient Cross-Entropy Method for Real-time Planning

Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Jan Achterhold, Joerg Stueckler, Michal Rolinek, Georg Martius

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.LG、cs.RO

Planning with Expectation Models

Yi Wan, Zaheer Abbas, Adam White, Martha White, Richard S. Sutton

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Flexible and Efficient Long-Range Planning Through Curious Exploration

Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin Feigelis, Daniel Yamins

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.RO

PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference

Masashi Okada, Norio Kosaka, Tadahiro Taniguchi

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.LG、cs.RO

Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems

Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi

专题命中 仿真与规划 ：latent dynamics(abstract)；分类 cs.LG、cs.RO

Comments Neural Information Processing Systems (NeurIPS) 2018

World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces

Marwin H. S. Segler

专题命中 仿真与规划 ：model-based RL(abstract)；分类 cs.LG；dynamics model(abstract)

Comments Accepted at the Generative Modeling and Model-Based Reasoning for Robotics and AI workshop at ICML 2019. Presented on June 14th 2019. See https://sites.google.com/view/mbrl-icml2019

Journal ref https://sites.google.com/view/mbrl-icml2019

The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces

G. Zacharias Holland, Erin J. Talvitie, Michael Bowling

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI；dynamics model(abstract)

The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach

Iulian Vlad Serban, Chinnadhurai Sankar, Michael Pieper, Joelle Pineau, Yoshua Bengio

专题命中 仿真与规划 ：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG

Comments 26 pages, 2 figures, 4 tables