Deep reinforcement learning approaches for process control

Spielberg, Steven; Gopaluni, R. Bhushan; Loewen, Philip D.

doi:10.1109/adconip.2017.7983780

Cited by 102 publications

(56 citation statements)

References 19 publications

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“…The implementation of Deep Learning Techniques for solving compound problems in the task of controls [17][18][19] in soft robotics has been one of the hottest topics of research. Hence, there has been the development of various algorithms that have surpassed the accuracy and precision of earlier approaches.…”

Section: Deep Learning In Soromentioning

confidence: 99%

Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges

et al. 2019

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The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics.

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Section: Deep Learning In Soromentioning

confidence: 99%

Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges

et al. 2019

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“…The learned controller is also tested on various other cases, such as the response to setpoint change and the effect of output and input noise. The results [11] are shown in Fig. 1.…”

Section: Simulation Resultsmentioning

confidence: 97%

“…Process control applications involve continuous states and action space. Policy gradient algorithms are widely used RL techniques to address continuous control problems [11]. We have used actor-critic based deterministic policy gradient similar to [11].…”

Section: Koksal Erenturkmentioning

confidence: 99%

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Design of “Deep Learning Controller”

Erenturk¹

2018

IJEAS

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Deep learning allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction mimicking how the brain perceives and understands multimodal information, thus implicitly capturing intricate structures of large-scale data. In the meantime, recent advances in deep learning, encompassing neural networks, hierarchical probabilistic models, and a variety of unsupervised and supervised feature learning algorithms, have brought about tremendous development to many areas of interest to the engineering community. In this work, an extended type of the current accomplishment of deep learning to chemical process control problem has been presented. As well-known, if one formulated the reward function properly, "deep learning" can be used for industrial process control purpose. The controller setup follows the typical reinforcement learning setup, whereby an agent (controller) interacts with an environment (process) through control actions and receives a reward in discrete time steps. Deep neural networks (DNN) serve as function approximators and are used to learn the control policies. Once the DNN trained, control actions can be achieved at the output of the learned network. Even though the policies are not explicitly specified for the DNN, the DNN has an ability to learn policies that are different from the traditional controllers. The designed "Deep Learning Controller" (DLC) for Single Input Single Output Systems (SISO) has been tested under various scenarios. Obtained results have been given in graphical illustrations for details and these results showed that DLC can be easily used for instead of any type of controller. Additionally, it can be concluded that DLC are very robust when compared with the other type of controllers in terms of noise and unknown disturbances.

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“…The implementation of Deep Learning Techniques for solving compound problems in the task of controls [16][17][18] in soft robotics has been one of the hottest topics of research. Hence, there has been development of various algorithms that have certainly surpassed the accuracy and precision of earlier approaches.…”

Section: Deep Learning In Soromentioning

confidence: 99%

Deep Reinforcement Learning for Soft Robotic Applications: Brief Overview with Impending Challenges

Bhagat¹,

Banerjee²,

Ren³

2018

Preprint

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The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to sprouting of a relatively new yet extremely rewarding sphere of technology. The fusion of current deep reinforcement algorithms with physical advantages of a soft bio-inspired structure certainly directs us to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment to achieve a task they have been assigned. For soft robotics structure possessing countless degrees of freedom, it is often not easy (something not even possible) to formulate mathematical constraints necessary for training a deep reinforcement learning (DRL) agent for the task in hand, hence, we resolve to imitation learning techniques due to ease of manually performing such tasks like manipulation that could be comfortably mimicked by our agent. Deploying current imitation learning algorithms on soft robotic systems have been observed to provide satisfactory results but there are still challenges in doing so. This review article thus posits an overview of various such algorithms along with instances of them being applied to real world scenarios and yielding state-of-the-art results followed by brief descriptions on various pristine branches of DRL research that may be centers of future research in this field of interest.

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Deep reinforcement learning approaches for process control

Cited by 102 publications

References 19 publications

Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges

Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges

Design of “Deep Learning Controller”

Deep Reinforcement Learning for Soft Robotic Applications: Brief Overview with Impending Challenges

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