Observable Markov Decision Process

Observable Markov Decision Processes (POMDPs) provide a powerful framework for modeling and solving sequential decision-making problems under uncertainty, where the complete state of the environment is not always known. Current research focuses on developing efficient algorithms to solve POMDPs, particularly for complex applications like autonomous driving, robotics, and human-robot interaction, often employing hierarchical planning, deep reinforcement learning, and Monte Carlo tree search. These advancements are significantly impacting various fields by enabling more robust and adaptable autonomous systems capable of operating effectively in uncertain and dynamic environments.

Papers

June 10, 2022

ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Haozhe Wang, Chao Du, Panyan Fang, Shuo Yuan, Xuming He, Liang Wang, Bo Zheng
Observable Markov Decision Process Curriculum Reinforcement Learning Paper Bidding Cost Constraint Real Time Bidding Ad Market

May 24, 2022

History Compression via Language Models in Reinforcement Learning
Fabian Paischer, Thomas Adler, Vihang Patil, Angela Bitto-Nemling, Markus Holzleitner, Sebastian Lehner, Hamid Eghbal-zadeh, Sepp Hochreiter
Language Model Reinforcement Learning Data Compression Observable Markov Decision Process Token Embeddings Language Transformer

May 23, 2022

Flow-based Recurrent Belief State Learning for POMDPs
Xiaoyu Chen, Yao Mu, Ping Luo, Shengbo Li, Jianyu Chen
Variational Inference Observable Markov Decision Process Flow Based Partially Observable Markov Decision Process C POMDPs Belief Distribution

March 31, 2022

Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks
Yang Shao, Quan Kong, Tadayuki Matsumura, Taiki Fuji, Kiyoto Ito, Hiroyuki Mizuno
Deep Reinforcement Learning Observable Markov Decision Process Partially Observable Markov Decision Process C POMDPs

March 9, 2022

March 3, 2022

SMA-NBO: A Sequential Multi-Agent Planning with Nominal Belief-State Optimization in Target Tracking
Tianqi Li, Lucas W. Krakow, Swaminathan Gopalswamy
Multi Agent Single Agent Belief State Observable Markov Decision Process Multi Agent Planning Target Tracking Multi Agent Sequential Decision Next Active Object

February 22, 2022

SIPOMDPLite-Net: Lightweight, Self-Interested Learning and Planning in POSGs with Sparse Interactions
Gengyu Zhang, Prashant Doshi
Task Planning Observable Markov Decision Process Observable Stochastic Game Self Interested Agent Sparse High Order Interaction Self Paced Learning

January 21, 2022

Under-Approximating Expected Total Rewards in POMDPs
Alexander Bork, Joost-Pieter Katoen, Tim Quatmann
Observable Markov Decision Process Partially Observable Markov Decision Process C POMDPs Reward Prediction Belief MDP

December 17, 2021

Compositional Learning-based Planning for Vision POMDPs
Sampada Deglurkar, Michael H. Lim, Johnathan Tucker, Zachary N. Sunberg, Aleksandra Faust, Claire J. Tomlin
Observable Markov Decision Process Partially Observable Markov Decision Process POMDP Policy

November 30, 2021

The Power of Communication in a Distributed Multi-Agent System
Philipp Dominic Siedler
Multi Agent System Real Power Timely Communication Observable Markov Decision Process Multi Agent Reinforcement