Undirected Networks
Finite-Sample Analysis for SARSA with Linear Function Approximation
Shaofeng Zou, Tengyu Xu, Yingbin Liang
SARSA is an on-policy algorithm to learn a Markov decision process policy in reinforcement learning. We investigate the SARSA algorithm with linear function approximation under the non-i.i.d.
Reconciling ฮป-Returns with Experience Replay
Brett Daley, Christopher Amato
A unique benefit to this approach is that each transition's TD error can be
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
Neural Information Processing Systems http://nips.cc/
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.