Causal reinforcement learning based on Bayesian networks applied to industrial settings

Valverde, Gabriel; Quesada, David; Larrañaga, Pedro; Bielza, Concha

doi:10.1016/j.engappai.2023.106657

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Robots can also learn from human-like behavior and adapt using RL [184,185]. Lastly, handling uncertainty in the environment is important and Bayesian RL helps robots make decisions while considering potential risks [186][187][188][189][190][191][192][193]. All of this has transformed how robots work in different fields, from manipulation to agile movement, making them smarter and more adaptable.…”

Section: Elevating Decision-making Processesmentioning

confidence: 99%

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Singh,

Kurukuru,

Khan

2023

Energies

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This paper provides a comprehensive review of the integration of advanced power management systems and learning techniques in the field of robotics. It identifies the critical roles these areas play in reshaping the capabilities of robotic systems across diverse applications. To begin, it highlights the significance of efficient power usage in modern robotics. The paper explains how advanced power converters effectively control voltage, manage current and shape waveforms, thereby optimizing energy utilization. These converters ensure that robotic components receive the precise voltage levels they require, leading to improved motor performance and enabling precise control over motor behavior. Consequently, this results in extended operational times and increased design flexibility. Furthermore, the review explores the integration of learning approaches, emphasizing their substantial impact on robotic perception, decision-making and autonomy. It discusses the application of techniques such as reinforcement learning, supervised learning and unsupervised learning, showcasing their applications in areas like object recognition, semantic segmentation, sensor fusion and anomaly detection. By utilizing these learning methods, robots become more intelligent, adaptable and capable of autonomous operation across various domains. By examining the interaction between advanced power management and learning integration, this review anticipates a future where robots operate with increased efficiency, adapt to various tasks and drive technological innovation across a wide range of industries.

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Section: Elevating Decision-making Processesmentioning

confidence: 99%

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Singh,

Kurukuru,

Khan

2023

Energies

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Causal Deep Q Networks

Khelifi,

Saki,

Faghihi

2024

Advances and Trends in Artificial Intelligence. Theory and Applications

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Bayesian Strategy Networks Based Soft Actor-Critic Learning

Yang,

Parasuraman

2024

ACM Trans. Intell. Syst. Technol.

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A strategy refers to the rules that the agent chooses the available actions to achieve goals. Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system’s utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method – soft actor-critic (SAC), and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. Our method achieves the state-of-the-art performance on the standard continuous control benchmarks in the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency. Furthermore, we extend the topic to the Multi-Agent systems (MAS), discussing the potential research fields and directions.

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Causal reinforcement learning based on Bayesian networks applied to industrial settings

Cited by 3 publications

References 28 publications

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Causal Deep Q Networks

Bayesian Strategy Networks Based Soft Actor-Critic Learning

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