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Related Concept Videos

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...
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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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Associative Learning

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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.
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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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Inverse Reinforcement Learning for Adversarial Apprentice Games.

Bosen Lian, Wenqian Xue, Frank L Lewis

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    This study introduces novel inverse reinforcement learning (RL) algorithms for adversarial games, enabling a learner to discover an expert's cost function from demonstrations. The methods are effective even with adversarial attacks and unknown system dynamics.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Control Theory

    Background:

    • Reinforcement learning (RL) is a powerful tool for decision-making.
    • Inverse RL (IRL) aims to infer reward functions from expert behavior.
    • Adversarial environments pose unique challenges for learning agents.

    Purpose of the Study:

    • To develop new IRL algorithms for Adversarial Apprentice Games.
    • To enable a learner to extract an expert's unknown cost function.
    • To address nonlinear systems and adversarial attacks in learning.

    Main Methods:

    • Developed a model-based IRL algorithm with two learning stages.
    • Proposed a model-free integral IRL algorithm using online demonstrations.
    • Implemented algorithms using neural networks (NNs).
    • Formulated two-player zero-sum games for adversarial agents.

    Main Results:

    • Demonstrated the effectiveness of both model-based and model-free IRL algorithms.
    • Showcased superiority over existing methods through simulations.
    • Confirmed that learned cost functions are stabilizing and not unique.
    • Validated algorithms in the presence of adversarial attacks.

    Conclusions:

    • The proposed IRL algorithms successfully reconstruct expert cost functions in adversarial settings.
    • Model-free approach offers flexibility by not requiring system dynamics.
    • Learned policies are stabilizing, providing robust control.
    • Algorithms show significant promise for complex learning scenarios.