Automatic Parameter Optimization Using Genetic Algorithm in Deep Reinforcement Learning for Robotic Manipulation Tasks

Sehgal, Adarsh; Ward, Nick; La, Hung Manh; Louis, Sushil J.

doi:10.48550/arxiv.2204.03656

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…According to the experimental findings, the suggested solution outperforms the original algorithm in terms of the learning performance both quickly and effectively. For six robotic manipulation tasks, FetchReach, FetchSlide, FetchPickAndPlace, DoorOpening, and AuboReach [ 145 ], they also suggest using a genetic algorithm (GA) technique to fine-tune the hyperparameters of deep deterministic policy gradient (DDPG) along with Hindsight Experience Replay (HER). The performance of the suggested GA+DDPG+HER approach is significantly higher than that of the current methods, resulting in a reduction in learning time.…”

Section: Deep Rl For Robotic Manipulationmentioning

confidence: 99%

A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation

Han

Mulyana

Stankovic³

et al. 2023

Sensors

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Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject.

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Section: Deep Rl For Robotic Manipulationmentioning

confidence: 99%

A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation

Han

Mulyana

Stankovic³

et al. 2023

Sensors

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show abstract

“…The outcomes in [31] demonstrated that the proposed algorithm was successful in locating a suitable feature subset, which also resulted in a high classification rate. Some other related works include: [22], [26], [13], and [25].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms

Sehgal¹,

Sehgal²,

La³

et al. 2022

Preprint

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Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world. Deep learning (DL) methods have shown to be very effective for mass detection in mammograms. Additional improvements of current DL models will further improve the effectiveness of these methods. A critical issue in this context is how to pick the right hyperparameters for DL models. In this paper, we present GA-E2E, a new approach for tuning the hyperparameters of DL models for brest cancer detection using Genetic Algorithms (GAs). Our findings reveal that differences in parameter values can considerably alter the area under the curve (AUC), which is used to determine a classifier's performance.

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