A Huber reward function-driven deep reinforcement learning solution for cart-pole balancing problem

Mishra, Shaili; Arora, Anuja

doi:10.1007/s00521-022-07606-6

Cited by 6 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the face of dynamic environments, the optimization of data quality and the design of reward functions become very important for motion planning [12]. The quality of input data refers to the efficiency of the algorithm in processing the data and the expressiveness of the data [13]. In dynamic environments, the direct training of input data may lead to overfitting or slow training due to the relative complexity of environmental information.…”

Section: Introductionmentioning

confidence: 99%

Robotic Manipulator in Dynamic Environment with SAC Combing Attention Mechanism and LSTM

Kuang,

Zhou

2024

Electronics

View full text Add to dashboard Cite

The motion planning task of the manipulator in a dynamic environment is relatively complex. This paper uses the improved Soft Actor Critic Algorithm (SAC) with the maximum entropy advantage as the benchmark algorithm to implement the motion planning of the manipulator. In order to solve the problem of insufficient robustness in dynamic environments and difficulty in adapting to environmental changes, it is proposed to combine Euclidean distance and distance difference to improve the accuracy of approaching the target. In addition, in order to solve the problem of non-stability and uncertainty of the input state in the dynamic environment, which leads to the inability to fully express the state information, we propose an attention network fused with Long Short-Term Memory (LSTM) to improve the SAC algorithm. We conducted simulation experiments and present the experimental results. The results prove that the use of fused neural network functions improved the success rate of approaching the target and improved the SAC algorithm at the same time, which improved the convergence speed, success rate, and avoidance capabilities of the algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%