Results 41 to 50 of about 311 (117)
Inverse Reinforcement Learning for Text Summarization
Findings of the Association for Computational Linguistics: EMNLP 2023, 2023 8 pages, 2 figures; accepted to Findings of EMNLP ...
Yu Fu 0009, Deyi Xiong, Yue Dong 0002
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Bounded Low Latency via Inverse Reinforcement Learning
IEEE AccessAccurate traffic prediction is essential for effective resource utilization and improving user experience quality in next generation wireless networks.
Hossein Shafieirad +3 more
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Hybrid Inverse Reinforcement Learning
CoRRThe inverse reinforcement learning approach to imitation learning is a double-edged sword. On the one hand, it can enable learning from a smaller number of expert demonstrations with more robustness to error compounding than behavioral cloning approaches.
Juntao Ren +4 more
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Inverse Reinforcement Learning from Failure
International Joint Conference on Autonomous Agents and Multiagent Systems, 2016 Inverse reinforcement learning (IRL) allows autonomous agents to learn to solve complex tasks from successful demonstrations. However, in many settings, e.g., when a human learns the task by trial and error, failed demonstrations are also readily available.
Shiarlis, K, Messias, J, Whiteson, S
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A Systematic Study on Reinforcement Learning Based Applications
Energies, 2023 We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems ...
Keerthana Sivamayil +5 more
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Scalable Bayesian Inverse Reinforcement Learning
CoRR, 2021 Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not scale well beyond the small tabular setting due to the need for an inner-loop MDP solver, and even non-Bayesian methods that do themselves scale often require extensive ...
Alex James Chan, Mihaela van der Schaar
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A Hierarchical Framework for Quadruped Robots Gait Planning Based on DDPG
Biomimetics, 2023 In recent years, significant progress has been made in employing reinforcement learning for controlling legged robots. However, a major challenge arises with quadruped robots due to their continuous states and vast action space, making optimal control ...
Yanbiao Li +4 more
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Active Exploration for Inverse Reinforcement Learning
Advances in Neural Information Processing Systems 35, 2022 Presented at Conference on Neural Information Processing Systems (NeurIPS ...
Lindner, David +2 more
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Identification of animal behavioral strategies by inverse reinforcement learning.
PLoS Computational Biology, 2018 Animals are able to reach a desired state in an environment by controlling various behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals' decision-making and is fundamental to dissect ...
Shoichiro Yamaguchi +6 more
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Adaptive Actuation of Magnetic Soft Robots Using Deep Reinforcement Learning
Advanced Intelligent Systems, 2023 Magnetic soft robots (MSRs) have attracted growing interest due to their unique advantages in untethered actuation and excellent controllability. However, actuation strategies of these robots have long been designed out of heuristics. Herein, it is aimed
Jianpeng Yao +6 more
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