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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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Observational Learning01:12

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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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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Science

    Background:

    • Deep reinforcement learning (DRL) excels in pattern recognition, robotics, and gaming.
    • Graph neural networks (GNNs) demonstrate superior performance in supervised learning for graph-structured data.
    • The integration of DRL and GNNs is a rapidly growing area of research.

    Purpose of the Study:

    • To provide a comprehensive review of hybrid DRL-GNN works.
    • To classify these works into algorithmic and application-specific contributions.
    • To analyze the benefits and challenges of fusing DRL and GNNs.

    Main Methods:

    • Categorization of existing DRL-GNN research.
    • Analysis of algorithmic and application-specific contributions.
    • Evaluation of generalizability and computational complexity improvements.

    Main Results:

    • The fusion of DRL and GNNs effectively addresses complex problems in engineering and life sciences.
    • Hybrid approaches enhance generalizability and reduce computational complexity.
    • Two main categories of contributions: algorithmic and application-specific.

    Conclusions:

    • The integration of DRL and GNNs offers significant potential for advancing AI.
    • Future research should focus on overcoming integration challenges.
    • This fusion is of broad interest to the machine learning community.