Context‐aware pub/sub control method using reinforcement learning

Single-track two-wheeled robots have become an important research topic in recent years, owing to their simple structure, energy savings and ability to run on narrow roads. However, the ramp jump remains a challenging task. In this study, we propose to realize a single-track two-wheeled robot ramp jump. We present a control method that employs continuous action reinforcement learning techniques for single-track two-wheeled robot control. We design a novel reward function for reinforcement learning, optimize the dimensions of the action space, and enable training under the deep deterministic policy gradient algorithm. Finally, we validate the control method through simulation experiments and successfully realize the single-track two-wheeled robot ramp jump task. Simulation results validate that the control method is effective and has several advantages over high-dimension action space control, reinforcement learning control of sparse reward function and discrete action reinforcement learning control.

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622 Control method of Inverted Pendulum with an unknown standing angle by reinforcement learning

The Proceedings of Conference of Hokuriku-Shinetsu Branch ◽

10.1299/jsmehs.2005.42.221 ◽

2005 ◽

Vol 2005.42 (0) ◽

pp. 221-222

Author(s):

Nobuo Yagihashi ◽

Matuzo Takamura

Keyword(s):

Reinforcement Learning ◽

Inverted Pendulum ◽

Control Method

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Context-aware reinforcement learning-based mobile cloud computing for telemonitoring

2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) ◽

10.1109/bhi.2018.8333459 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xiaoliang Wang ◽

Wei Wang ◽

Zhanpeng Jin

Keyword(s):

Cloud Computing ◽

Reinforcement Learning ◽

Mobile Cloud Computing ◽

Mobile Cloud ◽

Context Aware

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Reinforcement Learning for Active Noise Control in a Hydraulic System

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4049556 ◽

2021 ◽

Vol 143 (6) ◽

Author(s):

Eric R. Anderson ◽

Brian L. Steward

Keyword(s):

Reinforcement Learning ◽

Hydraulic System ◽

Noise Control ◽

Control Method ◽

Active Noise Control ◽

Operating Point ◽

Hydraulic Pressure ◽

Hydraulic Systems ◽

Active Noise ◽

Pressure Ripple

Abstract Hydraulic pressure ripple in a pump, as a result of converting rotational power to fluid power, continues to be a problem faced when developing hydraulic systems due to the resulting noise generated. In this paper, we present simulation results from leveraging an actor-critic reinforcement learning method as the control method for active noise control in a hydraulic system. The results demonstrate greater than 96%, 81%, and 61% pressure ripple reduction for the first, second, and third harmonics, respectively, in a single operating point test, along with the advantage of feed forward like control for high bandwidth response during dynamic changes in the operating point. It also demonstrates the disadvantage of long convergence times while the controller is effectively learning the optimal control policy. Additionally, this work demonstrates the ancillary benefit of the elimination of the injection of white noise for the purpose of system identification in the current state of the art.

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