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Reinforcement Schedules01:24

Reinforcement Schedules

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Rolling Resistance: Problem Solving01:17

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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Updated: Jan 14, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Multiagent Deep Reinforcement Learning With Evolutionary Strategy for Mobile Charging Vehicles Dispatching.

Hua Li, Bongju Jeong

    IEEE Transactions on Neural Networks and Learning Systems
    |January 12, 2026
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    Summary
    This summary is machine-generated.

    Mobile charging vehicles (MCVs) offer a flexible solution for electric vehicle (EV) charging. A new multi-agent deep reinforcement learning with evolutionary strategy (MARL-ES) framework optimizes MCV dispatching for improved efficiency and profitability.

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    Area of Science:

    • Intelligent transportation systems
    • Operations research
    • Artificial intelligence

    Background:

    • Fixed charging infrastructure limits electric vehicle (EV) services.
    • Dynamic dispatching of mobile charging vehicles (MCVs) is a promising solution.
    • Stochastic demand and high operational costs challenge current MCV dispatching.

    Purpose of the Study:

    • To develop an adaptive and efficient MCV dispatching strategy.
    • To address the limitations of static and conventional MCV dispatching methods.
    • To improve the balance between supply and demand in real-time EV charging services.

    Main Methods:

    • Formulated the MCV dispatching problem as a Markov decision process (MDP).
    • Proposed a novel multi-agent deep reinforcement learning (MARL) framework with evolutionary strategy (ES) (MARL-ES).
    • Utilized centralized training with decentralized execution (CTDE) and integrated action-space ESs, including mutation and segment-based crossover operators.

    Main Results:

    • MARL-ES significantly outperformed static optimization and conventional MARL approaches.
    • Demonstrated improvements in total profit, reduced operational costs, and minimized MCV moving distance.
    • Showcased robust scalability and adaptability across different fleet sizes and under uncertain conditions.

    Conclusions:

    • MARL-ES provides a practical and adaptive dispatching solution for intelligent mobile charging services (MCSs).
    • The framework effectively handles spatiotemporal variations in EV demand and MCV states.
    • This approach enhances the efficiency and economic viability of EV charging services.