Markov Models
Reinforcement Learning in Robust Markov Decision Processes
Shiau Hong Lim, Huan Xu, Shie Mannor
Neural Information Processing Systems http://nips.cc/
Multi-Prediction Deep Boltzmann Machines
Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio
Neural Information Processing Systems http://nips.cc/
Learning Chordal Markov Networks by Dynamic Programming
Kustaa Kangas, Mikko Koivisto, Teppo Niinimรคki
Neural Information Processing Systems http://nips.cc/
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
However, this restriction prevents it from representing value functions in which an agent's ordering over its actions can depend on
Optimistic Planning in Markov Decision Processes Using a Generative Model
Balรกzs Szรถrรฉnyi, Gunnar Kedenburg, Remi Munos
Neural Information Processing Systems http://nips.cc/
Markov Random Fields for Collaborative Filtering
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Sampling Networks and Aggregate Simulation for Online POMDP Planning
Hao(Jackson) Cui, Roni Khardon
Neural Information Processing Systems http://nips.cc/
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning
Erwan Lecarpentier, Emmanuel Rachelson
Our contribution can be presented in four points.
Learning Multiple Markov Chains via Adaptive Allocation
Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Using ideas from the Multi-Armed Bandit (MAB) literature, previous works (e.g., [
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation
Mingjun Zhong, Nigel Goddard, Charles Sutton
Neural Information Processing Systems http://nips.cc/