Action Policy - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Action Policy

The Experts below are selected from a list of 5316 Experts worldwide ranked by ideXlab platform

Qingyang Chen – 1st expert on this subject based on the ideXlab platform

  • Self-Learning Cruise Control Using Kernel-Based Least Squares Policy Iteration
    IEEE Transactions on Control Systems Technology, 2014
    Co-Authors: Jian Wang, Xin Xu, Qingyang Chen

    Abstract:

    This paper presents a novel learning-based cruise controller for autonomous land vehicles (ALVs) with unknown dynamics and external disturbances. The learning controller consists of a time-varying proportional-integral (PI) module and an actor-critic learning control module with kernel machines. The learning objective for the cruise control is to make the vehicle’s longitudinal velocity follow a smoothed spline-based speed profile with the smallest possible errors. The parameters in the PI module are adaptively tuned based on the vehicle’s state and the Action Policy of the learning control module. Based on the state transition data of the vehicle controlled by various initial policies, the Action Policy of the learning control module is optimized by kernel-based least squares Policy iteration (KLSPI) in an offline way. The effectiveness of the proposed controller was tested on an ALV platform during long-distance driving in urban traffic and autonomous driving on off-road terrain. The experimental results of the cruise control show that the learning control method can realize data-driven controller design and optimization based on KLSPI and that the controller’s performance is adaptive to different road conditions.

Dusit Niyato – 2nd expert on this subject based on the ideXlab platform

  • Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning
    2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019
    Co-Authors: Yang Zhang, Dusit Niyato

    Abstract:

    Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT), requiring increased decentralization and autonomous operation. To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. However, in complex and large-scale networks, the state and Action spaces are usually large, and existing Tabular Reinforcement Learning technique is unable to find the optimal state- Action Policy quickly. In this paper, deep reinforcement learning is proposed to overcome the mentioned shortcomings and allow a wireless gateway to derive an optimal Policy to maximize network throughput. When benchmarked against advanced DQN techniques, our proposed DQN configuration offers performance speedup of up to 1.8× with good overall performance.

Jian Wang – 3rd expert on this subject based on the ideXlab platform

  • Self-Learning Cruise Control Using Kernel-Based Least Squares Policy Iteration
    IEEE Transactions on Control Systems Technology, 2014
    Co-Authors: Jian Wang, Xin Xu, Qingyang Chen

    Abstract:

    This paper presents a novel learning-based cruise controller for autonomous land vehicles (ALVs) with unknown dynamics and external disturbances. The learning controller consists of a time-varying proportional-integral (PI) module and an actor-critic learning control module with kernel machines. The learning objective for the cruise control is to make the vehicle’s longitudinal velocity follow a smoothed spline-based speed profile with the smallest possible errors. The parameters in the PI module are adaptively tuned based on the vehicle’s state and the Action Policy of the learning control module. Based on the state transition data of the vehicle controlled by various initial policies, the Action Policy of the learning control module is optimized by kernel-based least squares Policy iteration (KLSPI) in an offline way. The effectiveness of the proposed controller was tested on an ALV platform during long-distance driving in urban traffic and autonomous driving on off-road terrain. The experimental results of the cruise control show that the learning control method can realize data-driven controller design and optimization based on KLSPI and that the controller’s performance is adaptive to different road conditions.