Observable Environment

Observable environments, characterized by incomplete or uncertain information, are a central challenge in artificial intelligence, particularly for autonomous agents making decisions and navigating complex tasks. Current research focuses on developing robust algorithms and model architectures, such as deep reinforcement learning with recurrent neural networks and belief-space planning, to address partial observability and improve decision-making in these environments. This research is crucial for advancing autonomous systems in various fields, including robotics, agriculture, and autonomous driving, where agents must operate effectively with limited sensory information. The development of more efficient and reliable methods for handling partial observability is key to unlocking the full potential of these technologies.

Papers

October 4, 2024

Towards Cost Sensitive Decision Making
Yang Li, Junier Oliva
C POMDPs Observable Environment POMDP Policy Price Sensitivity

August 2, 2024

Soil Sample Search in Partially Observable Environments
Han Yang, Andrew Dudash
Simulation Study Robotic Arm Unknown Environment Observable Environment Soil Variable Sub Center Soil Sampling

July 18, 2024

Visuospatial navigation without distance, prediction, or maps
Patrick Govoni, Pawel Romanczuk
Visual Navigation Sequential Information Sensory Input Observable Environment Path Finding Vision Based Navigation Common Bias Level Based Foraging

July 14, 2024

Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments
Siddharth Nayak, Adelmo Morrison Orozco, Marina Ten Have, Vittal Thirumalai, Jackson Zhang, Darren Chen, Aditya Kapoor, Eric Robinson, Karthik Gopalakrishnan, James Harrison, Brian Ichter, Anuj Mahajan, Hamsa Balakrishnan
Language Model Long Horizon Task Multi Agent Planning Observable Environment Long Horizon Planning AI Planning

July 6, 2024

Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments
Ainur Zhaikhan, Ali H. Sayed
Reinforcement Learning Observable Environment Social Learning

May 30, 2024

PDDLEGO: Iterative Planning in Textual Environments
Li Zhang, Peter Jansen, Tianyi Zhang, Peter Clark, Chris Callison-Burch, Niket Tandon
Task Planning High Level Plan Observable Environment Sequential Planning Symbolic Planner Text Based Environment Planning Representation

May 27, 2024

Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments
Saeedeh Ghanadbashi, Fatemeh Golpayegani
Autonomous Agent Observable Environment Ontology Based

March 17, 2024

PyroTrack: Belief-Based Deep Reinforcement Learning Path Planning for Aerial Wildfire Monitoring in Partially Observable Environments
Sahand Khoshdel, Qi Luo, Fatemeh Afghah
Observable Environment Fighting Fire Autonomous Unmanned Aerial Vehicle Wildfire Detection Neural Motion UAV Path Planning

March 5, 2024

CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments
Savitha Sam Abraham, Marjan Alirezaie, Luc De Raedt
Complex Reasoning Task Background Knowledge Observable Environment Perception Network

February 23, 2024

When in Doubt, Think Slow: Iterative Reasoning with Latent Imagination
Martin Benfeghoul, Umais Zahid, Qinghai Guo, Zafeirios Fountas
Observable Environment Iterative Reasoning State Prediction Iterative Inference Latent Imagination

February 14, 2024

Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learning
Michael Lanier, Ying Xu, Nathan Jacobs, Chongjie Zhang, Yevgeniy Vorobeychik
Deep Reinforcement Learning Partially Observable Markov Decision Process Observable Environment Interpretable Policy Supervised Reinforcement Learning

February 11, 2024

Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments
Jeongyeol Kwon, Liu Yang, Robert Nowak, Josiah Hanna
Reinforcement Learning Representation Learning Evidence Piece Long Term Memory Observable Environment Future Prediction Reinforcement Learning Objective History Representation

January 2, 2024

Learning-based agricultural management in partially observable environments subject to climate variability
Zhaoan Wang, Shaoping Xiao, Junchao Li, Jun Wang
Agricultural Domain Observable Environment Nash Equilibrium Policy Climate Variability Fertilizer Management Climate Scenario

December 12, 2023

Sequential Planning in Large Partially Observable Environments guided by LLMs
Swarna Kamal Paul
Medical LLM State Space Observable Environment Sequential Planning

December 6, 2023

Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment
Gokul Puthumanaillam, Xiangyu Liu, Negar Mehr, Melkior Ornik
LeArning Abstract Markov Decision Process Task Planning State Estimation Partial Observability Observable Environment Uncertain Dynamic Environment Time Varying Environment

October 23, 2023

Denoising Opponents Position in Partial Observation Environment
Aref Sayareh, Aria Sardari, Vahid Khoddami, Nader Zare, Vinicius Prado da Fonseca, Amilcar Soares
Long Short Term Memory Observable Environment Position Prediction Soccer Simulation 2 Dimensional Denoising Performance

September 25, 2023

Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments
Bernard Lange, Jiachen Li, Mykel J. Kochenderfer
Trajectory Prediction Occupancy Prediction Partial Observability Observable Environment Agent Trajectory Occlusion Sensitivity Analysis Occlusion Inference Scene Knowledge

September 19, 2023

Asymptotically Optimal Belief Space Planning in Discrete Partially-Observable Domains
Janis Eric Freund, Camille Phiquepal, Andreas Orthey, Marc Toussaint
Observable Environment Belief Space Planning Possible World Open Motion Planning Library

June 20, 2023

Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
Zixuan Wu, Sean Ye, Manisha Natarajan, Letian Chen, Rohan Paleja, Matthew C. Gombolay
Reinforcement Learning Web Tracking Observable Environment Conflict Detection Adversarial Search Sparse Text

June 9, 2023

Combining a Meta-Policy and Monte-Carlo Planning for Scalable Type-Based Reasoning in Partially Observable Environments
Jonathon Schwartz, Hanna Kurniawati, Marcus Hutter
Multi Agent System Autonomous Agent Monte Carlo Tree Search Observable Environment Policy Alignment Observable Non Deterministic Planning Monte Carlo Planning

Observable Environment

Papers

Towards Cost Sensitive Decision Making

Soil Sample Search in Partially Observable Environments

Visuospatial navigation without distance, prediction, or maps

Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments

Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments

PDDLEGO: Iterative Planning in Textual Environments

Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments

PyroTrack: Belief-Based Deep Reinforcement Learning Path Planning for Aerial Wildfire Monitoring in Partially Observable Environments

CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments

When in Doubt, Think Slow: Iterative Reasoning with Latent Imagination

Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learning

Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments

Learning-based agricultural management in partially observable environments subject to climate variability

Sequential Planning in Large Partially Observable Environments guided by LLMs

Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment

Denoising Opponents Position in Partial Observation Environment

Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments

Asymptotically Optimal Belief Space Planning in Discrete Partially-Observable Domains

Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment

Combining a Meta-Policy and Monte-Carlo Planning for Scalable Type-Based Reasoning in Partially Observable Environments