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Related Concept Videos

Reinforcement01:23

Reinforcement

172
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.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
172
Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Observational Learning01:12

Observational Learning

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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...
118
Associative Learning01:27

Associative Learning

276
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
276
Reinforcement Schedules01:24

Reinforcement Schedules

126
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.
Once a behavior is learned,...
126
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Cognition-Oriented Multiagent Reinforcement Learning.

Tenghai Qiu, Shiguang Wu, Zhen Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |March 3, 2025
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    Summary
    This summary is machine-generated.

    This study introduces a novel cognition-oriented multiagent reinforcement learning (CORL) framework. CORL enhances agent cooperation and performance by using local observations for situational and self-cognition, improving team coordination.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Cognitive Science

    Background:

    • Multiagent reinforcement learning (MARL) often struggles with effective cooperation using only local observations.
    • Psychological insights into individual behavior can inform agent cognition in MARL.
    • Existing MARL frameworks may lack mechanisms for precise role differentiation and team coordination.

    Purpose of the Study:

    • To propose a novel cognition-oriented multiagent reinforcement learning (CORL) framework.
    • To enhance agent cooperation and performance in MARL tasks by leveraging local observations.
    • To improve team coordination and role differentiation through advanced cognitive mechanisms.

    Main Methods:

    • Developed a CORL framework equipping agents with situational and self-cognition derived from local observations.
    • Introduced two information-theoretical regularizers to enhance cognition informativeness and precision.
    • Employed a centralized training with decentralized execution (CTDE) framework for policy network training.

    Main Results:

    • CORL demonstrated effective harnessing of local observations for enriched cooperation.
    • Significant performance improvements were observed, particularly in challenging multiagent tasks.
    • The proposed regularizers improved situational cognition alignment with global states and self-cognition with agent identities.

    Conclusions:

    • The CORL framework offers a promising approach to enhance cooperation in multiagent reinforcement learning.
    • Leveraging psychological insights and information-theoretic regularizers can significantly boost agent performance.
    • CORL provides a robust method for improved role differentiation and team coordination in complex MARL scenarios.