Results 271 to 280 of about 387,064 (296)

Goal-Conditioned Hierarchical Reinforcement Learning With High-Level Model Approximation

IEEE Transactions on Neural Networks and Learning Systems
Hierarchical reinforcement learning (HRL) exhibits remarkable potential in addressing large-scale and long-horizon complex tasks. However, a fundamental challenge, which arises from the inherently entangled nature of hierarchical policies, has not been ...
Yu Luo, Tianying Ji, Fuchun Sun, Huaping Liu, Jianwei Zhang, Mingxuan Jing, Wenbing Huang   +6 more
semanticscholar   +1 more source

Recent Advances in Hierarchical Reinforcement Learning

Discrete Event Dynamic Systems, 2003
This survey contains different interesting approaches on how to develop reinforcement learning (RL) algorithms in order to overcome the problem of exponentially growing dimensionality of parameters to be learned. It includes a brief classical description of the problem as well.
Barto, AG, Mahadevan, S
openaire   +3 more sources

Hierarchical Reinforcement Learning

2020
In this chapter, we introduce hierarchical reinforcement learning, which is a type of methods to improve the learning performance by constructing and leveraging the underlying structures of cognition and decision making process. Specifically, we first introduce the backgrounds and two primary categories of hierarchical reinforcement learning: options ...
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Hierarchical model-based reinforcement learning

Proceedings of the 25th international conference on Machine learning - ICML '08, 2008
Hierarchical decomposition promises to help scale reinforcement learning algorithms naturally to real-world problems by exploiting their underlying structure. Model-based algorithms, which provided the first finite-time convergence guarantees for reinforcement learning, may also play an important role in coping with the relative scarcity of data in ...
Nicholas K. Jong, Peter Stone
openaire   +1 more source

Hierarchical multi-agent reinforcement learning

Autonomous Agents and Multi-Agent Systems, 2001
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-agent reinforcement learning (RL) framework, and propose a hierarchical multi-agent RL algorithm called Cooperative HRL.
Ghavamzadeh, M, Mahadevan, S, Makar, R
openaire   +1 more source

Hierarchical Multiagent Reinforcement Learning

2004
Abstract : In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multiagent tasks. We introduce a hierarchical multiagent reinforcement learning (RL) framework and propose a hierarchical multiagent RL algorithm called Cooperative HRL.
Sridhar Mahadevan, Mohammad Ghavamzadeh
openaire   +1 more source

Hierarchical Adversarial Inverse Reinforcement Learning

IEEE Transactions on Neural Networks and Learning Systems
Imitation learning (IL) has been proposed to recover the expert policy from demonstrations. However, it would be difficult to learn a single monolithic policy for highly complex long-horizon tasks of which the expert policy usually contains subtask hierarchies.
Jiayu Chen, Tian Lan, Vaneet Aggarwal
openaire   +2 more sources

Hierarchical reinforcement learning and decision making

Current Opinion in Neurobiology, 2012
The hierarchical structure of human and animal behavior has been of critical interest in neuroscience for many years. Yet understanding the neural processes that give rise to such structure remains an open challenge. In recent research, a new perspective on hierarchical behavior has begun to take shape, inspired by ideas from machine learning, and in ...
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Hierarchical Reinforcement Learning with OMQ

2006 5th IEEE International Conference on Cognitive Informatics, 2006
A novel method of hierarchical reinforcement learning, named OMQ, by integrating Options into MAXQ is presented. In OMQ, the MAXQ is used as basic framework to design hierarchies experientially and learn online, and the Option is used to construct hierarchies automatically.
Jing Shen, Haibo Liu, Guochang Gu
openaire   +1 more source

Reinforcement active learning hierarchical loops

The 2011 International Joint Conference on Neural Networks, 2011
A curious agent, be it a robot, animal or human, acts so as to learn as much as possible about itself and its environment. Such an agent can also learn without external supervision, but rather actively probe its surrounding and autonomously induce the relations between its action's effects on the environment and the resulting sensory input.
Goren Gordon, Ehud Ahissar
openaire   +1 more source