Application of Big Data in Evaluation of Agricultural Products E-Commerce under the Background of Rural Revitalization

Sun, Jinli

doi:10.1155/2022/4381776

Cited by 1 publication

(2 citation statements)

References 25 publications

(21 reference statements)

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“…The policy network calculates the actions a to be taken in the current state mula ( 7 In formula (9), ( ) a s θ μ = can be calculated by formula (7).…”

Section: Basic Algorithm Of Strategy-based Reinforcement Learning 221...mentioning

confidence: 99%

“…In an intelligent decision-making environment, expert system technology proves invaluable for optimizing command plans, improving situational awareness, and ultimately facilitating efficient decision-making. Sun, L. et al introduced a joint decision system based on an expert system [7], which encompasses knowledge and rules from multiple fields and leverages the knowledge representation and reasoning capabilities of expert systems to support effective decision-making. The expert system possesses automatic characteristics [8] and requires minimal manual intervention.…”

Section: Introductionmentioning

confidence: 99%

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A Policy Gradient Algorithm to Alleviate the Multi-Agent Value Overestimation Problem in Complex Environments

Yang,

Li,

Hou

et al. 2023

Sensors

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Multi-agent reinforcement learning excels at addressing group intelligent decision-making problems involving sequential decision-making. In particular, in complex, high-dimensional state and action spaces, it imposes higher demands on the reliability, stability, and adaptability of decision algorithms. The reinforcement learning algorithm based on the multi-agent deep strategy gradient incorporates a function approximation method using discriminant networks. However, this can lead to estimation errors when agents evaluate action values, thereby reducing model reliability and stability and resulting in challenging convergence. With the increasing complexity of the environment, there is a decline in the quality of experience collected by the experience playback pool, resulting in low efficiency of the sampling stage and difficulties in algorithm convergence. To address these challenges, we propose an innovative approach called the empirical clustering layer-based multi-agent dual dueling policy gradient (ECL-MAD3PG) algorithm. Experimental results demonstrate that our ECL-MAD3PG algorithm outperforms other methods in various complex environments, demonstrating a remarkable 9.1% improvement in mission completion compared to MADDPG within the context of complex UAV cooperative combat scenarios.

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“…The policy network calculates the actions a to be taken in the current state mula ( 7 In formula (9), ( ) a s θ μ = can be calculated by formula (7).…”

Section: Basic Algorithm Of Strategy-based Reinforcement Learning 221...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Policy Gradient Algorithm to Alleviate the Multi-Agent Value Overestimation Problem in Complex Environments

Yang,

Li,

Hou

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Application of Big Data in Evaluation of Agricultural Products E-Commerce under the Background of Rural Revitalization

Cited by 1 publication

References 25 publications

A Policy Gradient Algorithm to Alleviate the Multi-Agent Value Overestimation Problem in Complex Environments

A Policy Gradient Algorithm to Alleviate the Multi-Agent Value Overestimation Problem in Complex Environments

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