Search results with tag "Re inforcement learning"
DRN: A Deep Reinforcement Learning Framework for News ...
www.personal.psu.edusimultaneously. Some recent attempts using reinforcement learn-ing in recommendation either do not model the future reward explicitly (MAB-based works [23, 43]), or use discrete user log to represent state and hence can not be scaled to large systems (MDP-based works [35, 36]). In contrast, our framework uses a DQN structure and can easily ...
Asynchronous Methods for Deep Reinforcement Learning
proceedings.mlr.pressThe General Reinforcement Learning Architecture (Gorila) of (Nair et al.,2015) performs asynchronous training of re-inforcement learning agents in a distributed setting. In Go-rila, each process contains an actor that acts in its own copy of the environment, a separate replay memory, and a learner
Mastering the Game of Go without Human Knowledge
discovery.ucl.ac.ukIn contrast, reinforcement learn-ing systems are trained from their own experience, in principle allowing them to exceed human capabilities, and to operate in domains where human expertise is lacking. Recently, there has been rapid progress towards this goal, using deep neural networks trained by reinforcement learning.
A Tutorial for Reinforcement Learning - Missouri S&T
web.mst.eduIf you find this tutorial or the codes in C and MATLAB (weblink provided below) useful, please do cite my book (for which this material was prepared), now in its second edition: A. Gosavi. Simulation-Based Optimization: Parametric Optimization Techniques and Re-inforcement Learning, Springer, New York, NY, Second edition, 2014.
Soft Actor-Critic: Off-Policy Maximum Entropy Deep ...
arxiv.orgMaximum entropy reinforcement learning optimizes poli-cies to maximize both the expected return and the ex-pected entropy of the policy. This framework has been used in many contexts, from inverse reinforcement learn-ing (Ziebart et al.,2008) to optimal control (Todorov,2008; Toussaint,2009;Rawlik et al.,2012). In guided policy