This repository provides a series of codes of dqn algorithms To be continued ... Reference Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning.[J]. Nature, 2015, 518(7540):529. https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow...
机器学习-52-RL-Tips of Q-Learning(强化学习-Q学习的一些技巧:Double DQN&Dueling DQN&Prioritized Reply&Multi-step等),程序员大本营,技术文章内容聚合第一站。
However, agents are often highly sensitive to the selection of the multi-step update horizon (n), and our empirical experiments show that a poorly chosen static value for n can in many cases lead to worse performance than single-step DQN. Inspired by the success of n-step DQN and the ...
Until now, the predominant approach in implementing AOD algorithms has involved the use of deep q-learning networks(DQNs), with a single discrete action as the output. Nevertheless, these methods exhibit shortcomings in both implementation efficiency and success rate. To address these challenges, an...
《Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target》J. F Hernandez-Garcia, R S. Sutton [University of Alberta] (2019) http://t.cn/E5mxfx2 view:http://t.cn/...
AftertheintroductionofthedeepQ-network(DQN)(Mnih etal.2013),whichcanplayAtarigamesonthehuman level,muchattentionhasbeenfocusedondeepreinforce- mentlearning(RL).Recently,deepRLisalsoapplied toavarietyofimageprocessingtasks(Lietal.2018; Lanetal.2018;Parketal.2018).However,thesemethods ...
train_dqn_queue_reward.py Add files via upload Jul 22, 2021 Deep-Reinforcement-Learning-for-Traffic-Signal-Control Agent design for single traffic signal; including DQN, Double DQN, Dueling DQN, PER, Noisy DQN, Multistep DQN, Distributional DQN and their combinations; ...
In this paper we combine the n n n -step action-value algorithms Retrace, Q Q Q -learning, Tree Backup, Sarsa, and Q(\sigma) Q(\sigma) with an architecture analogous to DQN. We test the performance of all these algorithms in the mountain car environment; this choice of environment ...
More importantly, we apply the idea of multistep reinforcement learning (RL) to SFC placement with dependencies for the first time and propose a novel SFC-suitable placement method named DQN-MSRA. Compared with the conventional one-step RL process that focuses on the impact of the current step...
We apply these methods to lenient deep reinforcement learning and propose the LSnDQN and LESnDQN algorithms, which we subsequently test in some extended variations of the coordinated multi-agent object transportation problem. Results show that LSnDQN has a great advantage on training with less ...