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How to train your robot with deep reinforcement learning: lessons we have learned [PDF]
International Journal of Robotics Research, 2021 Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated ...
Julian Ibarz, Jie Tan, Chelsea Finn
exaly +2 more sources
Exploration in deep reinforcement learning: A survey [PDF]
Information Fusion, 2022 This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems.
Pawel Ladosz, Minwoo Kim, Hyondong Oh
exaly +2 more sources
Survey on Knowledge Transfer Method in Deep Reinforcement Learning [PDF]
Jisuanji kexue, 2023 Deep reinforcement learning is a hot issue in artificial intelligence research.With the deepening of research,some shortcomings are gradually exposed,such as low data utilization,weak generalization ability,difficult exploration,lack of reasoning and ...
ZHANG Qiyang, CHEN Xiliang, CAO Lei, LAI Jun, SHENG Lei
doaj +1 more source
The Dormant Neuron Phenomenon in Deep Reinforcement Learning [PDF]
International Conference on Machine Learning, 2023 In this work we identify the dormant neuron phenomenon in deep reinforcement learning, where an agent's network suffers from an increasing number of inactive neurons, thereby affecting network expressivity.
Ghada Sokar +3 more
semanticscholar +1 more source
Deep Reinforcement Learning for Autonomous Driving: A Survey [PDF]
IEEE transactions on intelligent transportation systems (Print), 2020 With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments.
B. R. Kiran +6 more
semanticscholar +1 more source
On the Use of Deep Reinforcement Learning for Visual Tracking: A Survey
IEEE Access, 2021 This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning.
Giorgio Cruciata +2 more
doaj +1 more source
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning [PDF]
Neural Information Processing Systems, 2022 Program synthesis or code generation aims to generate a program that satisfies a problem specification. Recent approaches using large-scale pretrained language models (LMs) have shown promising results, yet they have some critical limitations.
Hung Le +4 more
semanticscholar +1 more source
Deep Reinforcement Learning with Double Q-Learning [PDF]
AAAI Conference on Artificial Intelligence, 2015 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 ...
H. V. Hasselt, A. Guez, David Silver
semanticscholar +1 more source
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
IEEE Access, 2023 While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to ...
Maziar Gomrokchi +4 more
doaj +1 more source
A Brief Survey of Deep Reinforcement Learning [PDF]
IEEE Signal Processing Magazine, 2017 Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world.
K. Arulkumaran +3 more
semanticscholar +1 more source