世界模型

Policy Optimization with Model-based Explorations

Feiyang Pan, Qingpeng Cai, An-Xiang Zeng, Chun-Xiang Pan, Qing Da, Hualin He, Qing He, Pingzhong Tang

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

Comments Accepted at AAAI-19

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

Lars Buesing, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, Nicolas Heess

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

Efficiently Learning Nonstationary Gaussian Processes for Real World Impact

Sahil Garg

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

Comments Draft is not suitable for public view at present. It requires significant additions of experiment results

Model-Free Linear Quadratic Control via Reduction to Expert Prediction

Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari

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

A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation

Behnoosh Parsa, Keshav Rajasekaran, Franziska Meier, Ashis G. Banerjee

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

Comments 38 pages, 9 figures

Generative Temporal Models with Spatial Memory for Partially Observed Environments

Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola

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

Comments ICML 2018

Accurate Uncertainties for Deep Learning Using Calibrated Regression

Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon

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

Comments ICML 2018

Structure Learning in Motor Control:A Deep Reinforcement Learning Model

Ari Weinstein, Matthew M. Botvinick

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

Comments 39th Annual Meeting of the Cognitive Science Society, to appear

Prediction and Control with Temporal Segment Models

Nikhil Mishra, Pieter Abbeel, Igor Mordatch

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

Comments camera-ready version, ICML 2017

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft

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

LFADS - Latent Factor Analysis via Dynamical Systems

David Sussillo, Rafal Jozefowicz, L. F. Abbott, Chethan Pandarinath

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

Comments 16 pages, 11 figures

Inference for SDE models via Approximate Bayesian Computation

Umberto Picchini

专题命中 模型式强化学习 ：latent dynamics(abstract)；dynamics model(abstract)

Comments Version accepted for publication in Journal of Computational & Graphical Statistics

Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation

Syogo Mori, Voot Tangkaratt, Tingting Zhao, Jun Morimoto, Masashi Sugiyama

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

Optimism in Reinforcement Learning and Kullback-Leibler Divergence

Sarah Filippi, Olivier Cappé, Aurélien Garivier

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

Comments This work has been accepted and presented at ALLERTON 2010; Communication, Control, and Computing (Allerton), 2010 48th Annual Allerton Conference on, Monticello (Illinois) : États-Unis (2010)