Asynchronous Methods For Deep Reinforcement
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车联网边缘计算环境下基于深度强化学习的分布 式服务卸载方法
cjc.ict.ac.cnA Deep Reinforcement Learning-BasedDistributed Service Offloading Method ... average service latency by 0.4% to 20.4% compared with four exiting service offloading methods in different IoV environments, proving the effectiveness and efficiency of D-SOAC. ... asynchronous advantage actor-critic 1 引言 ...
Asynchronous Methods for Deep Reinforcement Learning
proceedings.mlr.pressAsynchronous Methods for Deep Reinforcement Learning time than previous GPU-based algorithms, using far less resource than massively distributed approaches. The best of the proposed methods, asynchronous advantage actor-critic (A3C), also mastered a variety of continuous motor control tasks as well as learned general strategies for ex-
深度强化学习综述 - ict.ac.cn
cjc.ict.ac.cnoptimization methods to optimize the policies. In this part, we firstly highlight some pure policy gradient methods, then focus on a series of policy-based DRL algorithms which use the actor-critic framework e.g., Deep Deterministic Policy Gradient (DDPG), followed by an effective method named Asynchronous Advantage
TensorFlow: A System for Large-Scale Machine Learning
www.usenix.orgwith a focus on training and inference on deep neural net-works. Several Google services use TensorFlow in pro- ... commonly held belief that asynchronous replication is re-quired for scalable learning [14, 20, 49]. ... and reinforcement learning models, where the loss function is computed by some agent in a separate system, such as a video ...
Python code for Artificial Intelligence: Foundations of ...
artint.info1 Python code for Artificial Intelligence: Foundations of Computational Agents David L. Poole and Alan K. Mackworth Version 0.9.3 of November 13, 2021.