Transcription of Asynchronous Methods for Deep Reinforcement Learning
{{id}} {{{paragraph}}}
Example: confidence
Asynchronous Methods for Deep Reinforcement Learning
Asynchronous Methods for Deep Reinforcement LearningVolodymyr Puigdom nech P. DeepMind2 Montreal Institute for Learning Algorithms (MILA), University of MontrealAbstractWeproposeaconceptuallysi mpleandlightweight framework for deep reinforce-ment Learning that uses Asynchronous gradientdescent for optimization of deep neural networkcontrollers. We present Asynchronous variants offour standard Reinforcement Learning algorithmsand show that parallel actor-learners have astabilizing effect on training allowing all fourmethods to successfully train neural best performing method, anasynchronous variant of actor-critic, surpassesthe current state-of-the-art on the Atari domainwhile training for half the time on a singlemulti-core CPU instead of a GPU.
The process continues until the agent reaches a terminal state after which the process restarts. The return R t = P 1 k=0 kr t+k is the total accumulated return from time step twith discount factor 2(0;1]. The goal of the agent is to maximize the expected return from each state s t. The action value Qˇ(s;a) = E[R tjs t= s;a] is the ex-
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: