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Feb 26, 2017 · Inference reveals the number of features, the features, and the policies, allowing us to learn and to analyze the underlying structure of the ...
Request PDF | Bayesian Nonparametric Feature and Policy Learning for Decision-Making | Learning from demonstrations has gained increasing interest in the ...
02/26/17 - Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions b...
Jürgen T. Hahn, Abdelhak M. Zoubir: Bayesian Nonparametric Feature and Policy Learning for Decision-Making. CoRR abs/1702.08001 (2017).
Abstract. Most of the algorithms for inverse reinforcement learning (IRL) assume that the reward function is a linear function of the pre-defined state and ...
To address these issues, we propose PoissonGP, a novel Bayesian model that employs a non-homogeneous Poisson process with a Gaussian process prior for sales ...
The proposed method uses a Bayesian nonparametric mixture model to automatically partition the data and find a set of simple reward functions corresponding to ...
... decision process (POMDP) model M is an n-tuple. {S,A,O,T,Ω,R,γ}. S, A, and O are sets of states, actions, and observations. The state transi- tion function T ...
Missing: Feature | Show results with:Feature
We propose a Bayesian nonparametric approach to identifying useful composite features for learning the reward function. The composite features are assumed to be ...
Missing: Making | Show results with:Making
Bayesian Nonparametric Feature and Policy Learning for Decision-Making. In: Pattern Recognition (under review) Artikel, Bibliographie. Typ des Eintrags ...