partial-observability

Online Replanning in Belief Space for Partially Observable Task and Motion Problems

SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations

Solving pursuit-evasion problems on graphs using Reinfocement Learning and GNNs

Official PyTorch implementation of POEM (Partial Observation Experts Modelling) as introduced in the paper Contrastive Meta-Learning for Partially Observable Few-Shot Learning

MATLAB code for the paper "Assimilative Causal Inference".

UWM-JEPA: a JEPA world model with a density-matrix latent and learned unitary predictor for imagining hidden continuations under partial observability.

Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

Partially Observable Monte Carlo Planning algorithm (POMCP)

Comparing a full-map A* oracle with partially observable knowledge-based and genetic-policy agents in Wumpus World.

Decentralized traffic signal control under camera-limited observability (IEEE Access submission)

Hybrid planning agent for decision-making under partial observability (PG-WMA)

Various projects related to contingent planning under partial observability

End-to-end empirical implementation of model-based off-policy evaluation and pessimistic policy selection for confounded POMDPs (Hong, Qi & Xu, ICML 2024)

The AIS planner source code + benchmarks

Priced information-gathering and abstention evaluation in a synthetic partially observed city.

Operator-geometric framework for metabolic identifiability and metabolite panel design under partial observability using Human-GEM, proteomics-informed reaction weighting, and spectral geometry.

Minimal control-theoretic toy models for studying stability and failure modes under partial observability.

Controlled evaluation of persistent object memory for partially observable embodied agents in AI2-THOR.

Defender-side cyber incident simulator. An abstract state machine, not real infrastructure, and reinforcement-learning policies, not LLM agents. Host status is hidden behind noisy alerts, the attacker improvises, and a web console lets you play the same scenario by hand and compare your score to a trained agent's.

MATLAB code for the paper "Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference".