Markov Models
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs
Yanlin Han, Piotr Gmytrasiewicz
It extends POMDPs to multi-agent settings by including models of other agents in the state space and forming a hierarchical belief structure.
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making
Nishant Desai, Andrew Critch, Stuart J. Russell
Neural Information Processing Systems http://nips.cc/
Sampling Networks and Aggregate Simulation for Online POMDP Planning
Hao(Jackson) Cui, Roni Khardon
The solution in [5,4]requires aone-pass forward computation of marginal probabilities.
Monte-Carlo Tree Search for Constrained POMDPs
Jongmin Lee, Geon-hyeong Kim, Pascal Poupart, Kee-Eung Kim
Neural Information Processing Systems http://nips.cc/
Completing State Representations using Spectral Learning
Nan Jiang, Alex Kulesza, Satinder Singh
Neural Information Processing Systems http://nips.cc/
Learning Multiple Markov Chains via Adaptive Allocation
Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Neural Information Processing Systems http://nips.cc/