Reward-Punishment Actor-Critic Algorithm Applying to Robotic Non-grasping Manipulation

Kobayashi, Taisuke; Aotani, Takumi; Guadarrama-Olvera, Julio Rogelio; Dean-León, Emmanuel; Cheng, Gordon

doi:10.1109/devlrn.2019.8850699

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…DMP [5], [6] argued for the necessity of such a learning rule in back-propagating the true amount of negative signals, while explaining how pain-avoidance induces sample efficiency and exploration. RP-AC [16] is a revamped reward-punishment Actor-Critic framework that formulates a policy gradient for continuous control. Split-Q Learning [17] suggested a more generalized reward-punishment framework by parameterizing immediate positive and negative rewards and their approximated state-action-values, aligning with various neurological and psychiatric mechanisms.…”

Section: A Separating Reward and Punishmentmentioning

confidence: 99%

Modular deep reinforcement learning from reward and punishment for robot navigation

Wang¹,

Elfwing²,

Uchibe³

2021

Neural Networks

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Section: A Separating Reward and Punishmentmentioning

confidence: 99%

Modular deep reinforcement learning from reward and punishment for robot navigation

Wang¹,

Elfwing²,

Uchibe³

2021

Neural Networks

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“…To address this issue, Bodnar et al developed Quantile QT-Opt (Q2-Opt), a distributional variant of distributed Q-learning algorithm, for continuous domains and evaluated its performance in both simulated and real vision-based robotic grasping tasks[132]. Additionally, Kobayashi et al proposed a Reward-Punishment Actor-Critic (RP-AC) algorithm to optimize robot trajectory by acquiring suitable rewards[133], while Demura et al used the You Only Look Once (YOLO) object detection approach to identify the optimal grasp point for stable manipulation in their Q-Learning grasping motion acquisition method[134]. This technique enabled the robot to pick up the uppermost folded towel from a stack and place it on a table.Kim et al demonstrated that deep learning-based techniques with direct visual input can achieve state-of-the-art results for robotic grasping in a cluttered environment with diverse unseen target objects [73].…”

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confidence: 99%

Review of Reinforcement Learning for Robotic Grasping: Analysis and Recommendations

Sekkat,

Moutik,

Ourabah

et al. 2023

Stat., optim. inf. comput.

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This review paper provides a comprehensive analysis of over 100 research papers focused on the challenges of robotic grasping and the effectiveness of various machine learning techniques, particularly those utilizing Deep Neural Networks (DNNs) and Reinforcement Learning (RL). The objective of this review is to simplify the research process for others by gathering different forms of Deep Reinforcement Learning (DRL) grasping tasks in one place. Through a thorough analysis of the literature, the study emphasizes the critical nature of grasping for robots and how DRL techniques, particularly the Soft-Actor-Critic (SAC) strategy, have demonstrated high efficiency in handling the task. The results of this study hold significant implications for the development of more advanced and efficient grasping systems for robots. Continued research in this area is crucial to further enhance the capabilities of robots in handling complex and challenging tasks, such as grasping.

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Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

2020

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The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely, the intended applications, techniques applied, challenges faced by researchers and recommendations for minimizing obstacles. This review refers to all relevant articles on deep reinforcement learning-based object manipulation and solutions. The object grasping issue is a major manipulation challenge. Object grasping requires detection systems, methods and tools to facilitate efficient and fast agent training. Several studies have proposed that object grasping and its subtypes are the main elements in dealing with the environment and agent. Unlike other review articles, this review article provides different observations on deep reinforcement learning-based manipulation. The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important guidelines.

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Reward-Punishment Actor-Critic Algorithm Applying to Robotic Non-grasping Manipulation

Cited by 4 publications

References 16 publications

Modular deep reinforcement learning from reward and punishment for robot navigation

Modular deep reinforcement learning from reward and punishment for robot navigation

Review of Reinforcement Learning for Robotic Grasping: Analysis and Recommendations

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

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