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Deep Reinforcement Learning with Double Q-Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be ...
Hado van Hasselt +2 more
core +5 more sources
Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox [PDF]
Machine Intelligence Research, 2024
(https://link.springer.com/article/10.1007/s11633-023-1454-4), 2022 With the breakthrough of AlphaGo, deep reinforcement learning becomes a recognized technique for solving sequential decision-making problems. Despite its reputation, data inefficiency caused by its trial and error learning mechanism makes deep ...
Huang, Kaiqi +7 more
core +2 more sources
A Brief Survey of Deep Reinforcement Learning [PDF]
IEEE Signal Processing Magazine, 2017 Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from ...
Kai Arulkumaran +3 more
arxiv +9 more sources
Deep Ordinal Reinforcement Learning [PDF]
, 2019 Reinforcement learning usually makes use of numerical rewards, which have nice properties but also come with drawbacks and difficulties. Using rewards on an ordinal scale (ordinal rewards) is an alternative to numerical rewards that has received more ...
C Wirth, CJ Watkins, RS Sutton, V Mnih
core +4 more sources
Deep Reinforcement Learning that Matters
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to ...
Bachman, Philip +5 more
core +2 more sources
Deep Reinforcement Learning for Dialogue Generation
, 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
openalex +4 more sources
Deep Residual Reinforcement Learning
, 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 ...
Boehmer, Wendelin +2 more
core +3 more sources
Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning [PDF]
SensorsReinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment.
Yana Yang +4 more
doaj +2 more sources
Deep Reinforcement Learning for Swarm Systems [PDF]
Journal of Machine Learning Research, 2019 Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making ...
Hüttenrauch, Maximilian +2 more
core +4 more sources
Explainability in deep reinforcement learning [PDF]
Knowledge-Based Systems, 2021 Article accepted at Knowledge-Based ...
Heuillet, Alexandre +2 more
openaire +6 more sources