Results 21 to 30 of about 1,056,132 (188)
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 +3 more
doaj +1 more source
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 +5 more
semanticscholar +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 +6 more
semanticscholar +1 more source
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 +3 more
doaj +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 +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 +2 more
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Applications of Deep Reinforcement Learning in Communications and Networking: A Survey [PDF]
IEEE Communications Surveys and Tutorials, 2018 This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more ...
Nguyen Cong Luong +6 more
semanticscholar +1 more source
Multi-agent deep reinforcement learning: a survey
Artificial Intelligence Review, 2021 The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction ...
Sven Gronauer, K. Diepold
semanticscholar +1 more source