Results 21 to 30 of about 1,052,376 (334)

Rainbow: Combining Improvements in Deep Reinforcement Learning [PDF]

AAAI Conference on Artificial Intelligence, 2017
The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN
Matteo Hessel, Joseph Modayil, H. V. Hasselt, T. Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, M. G. Azar, David Silver   +9 more
semanticscholar   +1 more source

Characterizing Mobile Money Phishing Using Reinforcement Learning

IEEE Access, 2023
Mobile money helps people accumulate, send, and receive money using their mobile phones without having a bank account (i.e., in some African countries). Such technology is heavily and efficiently used in many areas where bank services are unavailable and/
Alima Nzeket Njoya, Vanelle Laure Tchaya Ngongag, Franklin Tchakounte, Marcellin Atemkeng, Claude Fachkha   +4 more
doaj   +1 more source

Feasibility Analysis and Application of Reinforcement Learning Algorithm Based on Dynamic Parameter Adjustment

Algorithms, 2020
Reinforcement learning, as a branch of machine learning, has been gradually applied in the control field. However, in the practical application of the algorithm, the hyperparametric approach to network settings for deep reinforcement learning still ...
Menglin Li, Xueqiang Gu, Chengyi Zeng, Yuan Feng   +3 more
doaj   +1 more source

Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Mendel, 2021
Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots.
Roman Parak, Radomil Matousek
doaj   +1 more source

Champion-level drone racing using deep reinforcement learning

Nature, 2023
An autonomous system is described that combines deep reinforcement learning with onboard sensors collecting data from the physical world, enabling it to fly faster than human world champion drone pilots around a race track.
Elia Kaufmann, L. Bauersfeld, Antonio Loquercio, Matthias Müller, V. Koltun, Davide Scaramuzza   +5 more
semanticscholar   +1 more source

Research on Energy Management Strategy of a Hybrid Commercial Vehicle Based on Deep Reinforcement Learning

World Electric Vehicle Journal, 2023
Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed.
Jianguo Xi, Jingwei Ma, Tianyou Wang, Jianping Gao   +3 more
doaj   +1 more source

Target-driven visual navigation in indoor scenes using deep reinforcement learning [PDF]

IEEE International Conference on Robotics and Automation, 2016
Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new goals, and (2) data inefficiency, i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it ...
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, A. Gupta, Li Fei-Fei, Ali Farhadi   +6 more
semanticscholar   +1 more source

Deep Reinforcement Learning for Dialogue Generation [PDF]

Conference on Empirical Methods in Natural Language Processing, 2016
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.
Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, Jianfeng Gao   +5 more
semanticscholar   +1 more source

Deep Residual Reinforcement Learning

International Joint Conference on Autonomous Agents and Multiagent Systems, 2020
We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark.
Zhang, S, Boehmer, W, Whiteson, S
openaire   +4 more sources

Deep Reinforcement Learning for Trading [PDF]

The Journal of Financial Data Science, 2020
16 pages, 3 ...
Zihao Zhang, Stefan Zohren, Stephen J. Roberts   +2 more
openaire   +2 more sources