Markov Models
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar, Rahul Jain
A naive approach to an unknown model is the certainty equivalence principle .
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, Pieter Abbeel
These counts are then used to compute a reward bonus according to the classic count-based exploration theory. We find that simple hash functions can achieve surprisingly good results on many challenging tasks. Furthermore, we show that a domain-dependent learned hash code may further improve these results.
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Anton Osokin, Francis Bach, Simon Lacoste-Julien
Exponential number of classes: this brings both additional computational and statistical challenges. By exponential, we mean exponentially large in the size of the natural dimension of output, e.g., the
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting
Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu
Neural Information Processing Systems http://nips.cc/
Predictive State Recurrent Neural Networks
Carlton Downey, Ahmed Hefny, Byron Boots, Geoffrey J. Gordon, Boyue Li
Due to their probabilistic grounding, BFs and PSRs possess a strong statistical theory leading to efficient learning algorithms.
A multi-agent reinforcement learning model of common-pool resource appropriation
Humanity faces numerous problems of common-pool resource appropriation.
DPSCREEN: Dynamic Personalized Screening
Kartik Ahuja, William Zame, Mihaela van der Schaar
Screening is important for the diagnosis and treatment of a wide variety of diseases. A good screening policy should be personalized to the features of the patient and to the dynamic history of the patient (including the history of screening).
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Taylor W. Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings.
Neural Variational Inference and Learning in Undirected Graphical Models
Volodymyr Kuleshov, Stefano Ermon
Our approach offers a number of advantages over previous methods.