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r2023b reinforcement learning toolbox  (MathWorks Inc)


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    MathWorks Inc r2023b reinforcement learning toolbox
    R2023b Reinforcement Learning Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 94/100, based on 76 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/reinforcement+learning+toolbox/Reinforcement+Learning+Toolbox/10__1007_slash_s11581___025___06939___1-119-16-15
    Average 94 stars, based on 76 article reviews
    r2023b reinforcement learning toolbox - by Bioz Stars, 2026-09
    94/100 stars

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    Article Title: Deep reinforcement learning enhanced PID control for hydraulic servo systems in injection molding machines
    Article Snippet: The reinforcement learning controller is developed using the Reinforcement Learning Toolbox in MATLAB/Simulink.

    Article Title: Modeling, Positioning, and Deep Reinforcement Learning Path Following Control of Scaled Robotic Vehicles: Design and Experimental Validation
    Article Snippet: Mobile robotic systems serve as versatile platforms for diverse indoor applications, ranging from warehousing and manufacturing to test benches dedicated to evaluating automated driving (AD) functions.. In AD systems, the path following (PF) layer is responsible for defining steering commands to follow the reference path.. Recently explored approaches involve artificial intelligence-based methods, such as Deep Reinforcement Learning (DRL).

    Article Title: Research on robot path planning and obstacle avoidance algorithm in dynamic environment based on deep reinforcement learning
    Article Snippet: Furthermore, the research is conducted in a MATLAB environment, leveraging the Reinforcement Learning Toolbox to implement and train the models.

    Article Title: Deep reinforcement learning enhanced PID control for hydraulic servo systems in injection molding machines.
    Article Snippet: The reinforcement learning controller is developed using the Reinforcement Learning Toolbox in MATLAB/ Simulink.

    Article Title: An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning
    Article Snippet: The actor and critic were designed and trained using the Reinforcement Learning Toolbox in MATLAB 2023a (Mathworks, USA).

    Article Title: Data‐Driven Sliding Mode Control for Partially Unknown Nonlinear Systems
    Article Snippet: The model is trained using MATLAB’s Reinforcement Learning Toolbox.

    Article Title: Adaptive deep Q-networks for accurate electric vehicle range estimation
    Article Snippet: Reinforcement Learning Toolbox , R2024a , MathWorks, Inc. , Academic (included with MATLAB license) , Development and training of the Deep Q-Learning (DQN) framework.

    Article Title: Research on robot path planning and obstacle avoidance algorithm in dynamic environment based on deep reinforcement learning
    Article Snippet: The Reinforcement Learning Toolbox in MATLAB provides a comprehensive array of features, including model training, debugging, and performance assessment, so as to facilitate the development and optimization of intricate reinforcement learning algorithms.



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    Image Search Results


    MATLAB/Simulink implementation block diagram of the proposed control system using reinforcement learning Twin-Delayed Deep Deterministic agent.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

    doi: 10.3390/s25165055

    Figure Lengend Snippet: MATLAB/Simulink implementation block diagram of the proposed control system using reinforcement learning Twin-Delayed Deep Deterministic agent.

    Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

    Techniques: Blocking Assay, Control

    System response in MATLAB/Simulink using reinforcement learning Twin-Delayed Deep Deterministic Agent when the level decreases.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

    doi: 10.3390/s25165055

    Figure Lengend Snippet: System response in MATLAB/Simulink using reinforcement learning Twin-Delayed Deep Deterministic Agent when the level decreases.

    Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

    Techniques:

    System response in MATLAB/Simulink using reinforcement learning Twin-Delayed Deep Deterministic Agent when the level increases.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

    doi: 10.3390/s25165055

    Figure Lengend Snippet: System response in MATLAB/Simulink using reinforcement learning Twin-Delayed Deep Deterministic Agent when the level increases.

    Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

    Techniques:

    Response for reinforcement learning + Particle Swarm Optimization and PID System Response when the level increases.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

    doi: 10.3390/s25165055

    Figure Lengend Snippet: Response for reinforcement learning + Particle Swarm Optimization and PID System Response when the level increases.

    Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

    Techniques:

    Response for reinforcement learning + Particle Swarm Optimization and PID System Response when the level decreases.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

    doi: 10.3390/s25165055

    Figure Lengend Snippet: Response for reinforcement learning + Particle Swarm Optimization and PID System Response when the level decreases.

    Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

    Techniques:

    Fig. 2 Trial-and-error cycle in reinforcement learning

    Journal: Mechanika

    Article Title: Real-Time Swing-up of a Linear Inverted Pendulum Using Reinforcement Learning

    doi: 10.5755/j02.mech.39202

    Figure Lengend Snippet: Fig. 2 Trial-and-error cycle in reinforcement learning

    Article Snippet: To develop expertise in using the MATLAB Reinforcement Learning Toolbox, particularly in the implementation of the DDPG algorithm.

    Techniques: