[PDF][PDF] Lifetime value marketing using reinforcement learning
G Theocharous, A Hallak - 2013 - Citeseer
In many marketing applications, companies use technology for interacting with their
customers and making product or services recommendations. Today, these marketing …
customers and making product or services recommendations. Today, these marketing …
Barrier-certified adaptive reinforcement learning with applications to brushbot navigation
M Ohnishi, L Wang, G Notomista… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
This paper presents a safe learning framework that employs an adaptive model learning
algorithm together with barrier certificates for systems with possibly nonstationary agent …
algorithm together with barrier certificates for systems with possibly nonstationary agent …
A kernel-based approach to non-stationary reinforcement learning in metric spaces
In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in non-
stationary Markov Decision Processes (MDPs) whose state-action set is endowed with a …
stationary Markov Decision Processes (MDPs) whose state-action set is endowed with a …
Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains
Model-based strategies for control are critical to obtain sample efficient learning. Dyna is a
planning paradigm that naturally interleaves learning and planning, by simulating one-step …
planning paradigm that naturally interleaves learning and planning, by simulating one-step …
Unlocking the power of representations in long-term novelty-based exploration
We introduce Robust Exploration via Clustering-based Online Density Estimation
(RECODE), a non-parametric method for novelty-based exploration that estimates visitation …
(RECODE), a non-parametric method for novelty-based exploration that estimates visitation …
Practical kernel-based reinforcement learning
Kernel-based reinforcement learning (KBRL) stands out among approximate reinforcement
learning algorithms for its strong theoretical guarantees. By casting the learning problem as …
learning algorithms for its strong theoretical guarantees. By casting the learning problem as …
Quantized attention-gated kernel reinforcement learning for brain–machine interface decoding
F Wang, Y Wang, K Xu, H Li, Y Liao… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
Reinforcement learning (RL)-based decoders in brain-machine interfaces (BMIs) interpret
dynamic neural activity without patients' real limb movements. In conventional RL, the goal …
dynamic neural activity without patients' real limb movements. In conventional RL, the goal …
Off-policy reinforcement learning with gaussian processes
An off-policy Bayesian nonparameteric approximate reinforcement learning framework,
termed as GPQ, that employs a Gaussian processes (GP) model of the value (Q) function is …
termed as GPQ, that employs a Gaussian processes (GP) model of the value (Q) function is …
Compressed conditional mean embeddings for model-based reinforcement learning
We present a model-based approach to solving Markov decision processes (MDPs) in which
the system dynamics are learned using conditional mean embeddings (CMEs). This class of …
the system dynamics are learned using conditional mean embeddings (CMEs). This class of …
A self-taught artificial agent for multi-physics computational model personalization
Personalization is the process of fitting a model to patient data, a critical step towards
application of multi-physics computational models in clinical practice. Designing robust …
application of multi-physics computational models in clinical practice. Designing robust …