Agent Modelling under Partial Observability for Deep Reinforcement Learning

16 June 2020

Papers citing "Agent Modelling under Partial Observability for Deep Reinforcement Learning"

8 / 8 papers shown

Title
MADiff: Offline Multi-agent Learning with Diffusion Models Zhengbang Zhu Minghuan Liu Liyuan Mao Bingyi Kang Minkai Xu Yong Yu Stefano Ermon Weinan Zhang DiffM OffRL 80 34 0 03 Jan 2025
Using High-Level Patterns to Estimate How Humans Predict a Robot will Behave Sagar Parekh Lauren Bramblett N. Bezzo Dylan P. Losey 32 0 0 20 Sep 2024
Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction Kaiqi Chen Jing Yu Lim Kingsley Kuan Harold Soh 27 0 0 12 Aug 2023
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning Guy Azran Mohamad H. Danesh Stefano V. Albrecht Sarah Keren AI4CE 29 1 0 11 Jul 2023
Latent Interactive A2C for Improved RL in Open Many-Agent Systems Keyang He Prashant Doshi Bikramjit Banerjee OffRL 23 3 0 09 May 2023
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning Pedro P. Santos Diogo S. Carvalho Miguel Vasco Alberto Sardinha Pedro A. Santos Ana Paiva Francisco S. Melo 19 1 0 12 Oct 2022
Learning Generalizable Risk-Sensitive Policies to Coordinate in Decentralized Multi-Agent General-Sum Games Ziyi Liu Xian Guo Yongchun Fang 18 0 0 31 May 2022
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015