Emergency-Response Locomotion of Hexapod Robot with Heuristic Reinforcement Learning Using Q-Learning

Yang, Ming-Chieh; Samani, Hooman; Zhu, Kening

doi:10.1007/978-3-030-26118-4_31

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Heuristic RL uses pieces of information that generalise over an area of the state space. The information is used to assist the agent in decision-making and reduce the searchable state space [93,94]. An example of a heuristic is a rule.…”

Section: Heuristic Reinforcement Learningmentioning

confidence: 99%

A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review

Bignold,

Cruz,

Taylor

et al. 2020

Preprint

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A long-term goal of reinforcement learning agents is to be able to perform tasks in complex real-world scenarios. The use of external information is one way of scaling agents to more complex problems. However, there is a general lack of collaboration or interoperability between different approaches using external information. In this work, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering such collaboration by classifying and comparing various methods that use external information in the learning process. The proposed taxonomy details the relationship between the external information source and the learner agent, highlighting the process of information decomposition, structure, retention, and how it can be used to influence agent learning. As well as reviewing state-of-the-art methods, we identify current streams of reinforcement learning that use external information in order to improve the agent's performance and its decision-making process. These include heuristic reinforcement learning, interactive reinforcement learning, learning from demonstration, transfer learning, and learning from multiple sources, among others. These streams of reinforcement learning operate with the shared objective of scaffolding the learner agent. Lastly, we discuss further possibilities for future work in the field of assisted reinforcement learning systems.

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Section: Heuristic Reinforcement Learningmentioning

confidence: 99%

A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review

Bignold,

Cruz,

Taylor

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

A conceptual framework for externally-influenced agents: an assisted reinforcement learning review

Bignold

Cruz

Taylor

et al. 2021

J Ambient Intell Human Comput

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Load Balancing Algorithm of Controller Based on SDN Architecture Under Machine Learning

Liang

Wen-li

Zhao

et al. 2020

Journal of Systems Science and Information

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With the rapid development of cloud computing and other related services, higher requirements are put forward for network transmission and delay. Due to the inherent distributed characteristics of traditional networks, machine learning technology is difficult to be applied and deployed in network control. The emergence of SDN technology provides new opportunities and challenges for the application of machine learning technology in network management. A load balancing algorithm of Internet of things controller based on data center SDN architecture is proposed. The Bayesian network is used to predict the degree of load congestion, combining reinforcement learning algorithm to make optimal action decision, self-adjusting parameter weight to adjust the controller load congestion, to achieve load balance, improve network security and stability.

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Emergency-Response Locomotion of Hexapod Robot with Heuristic Reinforcement Learning Using Q-Learning

Cited by 3 publications

References 8 publications

A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review

A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review

A conceptual framework for externally-influenced agents: an assisted reinforcement learning review

Load Balancing Algorithm of Controller Based on SDN Architecture Under Machine Learning

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