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Crowd Evacuation Simulation Using Hierarchical Deep Reinforcement Learning
2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2021Data-driven crowd evacuation learning methods are often used to enhance the realism of crowd simulation. However, the learning results of traditional methods cannot adapt to the dynamic changes of the simple scene, and thus have the disadvantage of poor generalization.
Zheng Zhang +4 more
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Evaluating Adaptation Performance of Hierarchical Deep Reinforcement Learning
2020 IEEE International Conference on Robotics and Automation (ICRA), 2020Deep Reinforcement Learning has been used to exploit specific environments, but has difficulty transferring learned policies to new situations. This issue poses a problem for practical applications of Reinforcement Learning, as real-world scenarios may introduce unexpected differences that drastically reduce policy performance.
Neale Van Stolen +3 more
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Deep Hierarchical Communication Graph in Multi-Agent Reinforcement Learning
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023Sharing intentions is crucial for efficient cooperation in communication-enabled multi-agent reinforcement learning. Recent work applies static or undirected graphs to determine the order of interaction. However, the static graph is not general for complex cooperative tasks, and the parallel message-passing update in the undirected graph with cycles ...
Zeyang Liu +5 more
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Deep Belief Network for Modeling Hierarchical Reinforcement Learning Policies
2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013Intelligent agents over their lifetime face multiple tasks that require simultaneous modeling and control of complex, initially unknown environments, observed via incomplete and uncertain observations. In such scenarios, policy learning is subject to the curse of dimensionality, leading to scaling problems for traditional Reinforcement Learning (RL ...
Predrag D. Djurdjevic, Manfred Huber
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Hierarchical Gait Generation for Modular Robots Using Deep Reinforcement Learning
2021 IEEE International Conference on Mechatronics (ICM), 2021Modular robots have the ability to perform versatile locomotion with a high diversity of morphologies. However, designing robust locomotion gaits for arbitrary robot morphologies remains exceptionally challenging. In this paper, a two-level hierarchical locomotion framework is presented for addressing modular robot locomotion tasks.
Jiayu Wang, Chuxiong Hu, Yu Zhu
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An Energy-Efficient Hardware Accelerator for Hierarchical Deep Reinforcement Learning
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2021Reinforcement Learning (RL) has shown great performance in solving sequential decision-making and control in dynamic environments problems. Despite its achievements, training Deep Neural Network (DNN) based RL is expensive in terms of time and power because of the large number of episodes required to train agents with high dimensional image ...
Aidin Shiri +5 more
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Deep Hierarchical Variational Autoencoders for World Models in Reinforcement Learning
2023 Fifth International Conference on Transdisciplinary AI (TransAI), 2023With the increasing demand for sample-efficient and robust reinforcement learning agents, particularly in intricate domains like robotics, healthcare, and gaming, there is a strong need to minimize the computational overhead caused by the interactions between real and virtual agents.
Ayyalasomayajula, Sriharshitha +2 more
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Accelerating Deep Reinforcement Learning via Hierarchical State Encoding with ELMs
2021Image-based deep reinforcement learning has made great breakthrough and achievements in recent years, while unavoidably facing with the requirement of a large amount of interaction data and the problem of low training efficiency. In order to refine this problem, we propose a new method to accelerate the learning process of deep reinforcement learning ...
Tao Tang, Qiang Fang, Xin Xu, Yujun Zeng
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When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning
Cybernetics and Systems, 2019AbstractMulti-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions t...
Marie Ossenkopf +2 more
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Deep Hierarchical Reinforcement Learning for Autonomous Driving with Distinct Behaviors
2018 IEEE Intelligent Vehicles Symposium (IV), 2018Deep reinforcement learning has achieved great progress recently in domains such as learning to play Atari games from raw pixel input. The model-free characteristics of reinforcement learning free us from hand-encoding complex policies. However, for real world tasks such as autonomous driving, there are some complex sequential decision making processes
Jianyu Chen +2 more
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