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Related Concept Videos

Observational Learning01:12

Observational Learning

158
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...
158
Role of Shaping in Operant Conditioning01:19

Role of Shaping in Operant Conditioning

284
Shaping is a technique used in operant conditioning to train complex behaviors by rewarding successive approximations toward the target behavior. This method is necessary because organisms are unlikely to perform complex behaviors spontaneously. Instead, shaping breaks down the desired behavior into small, manageable steps.
The steps involved in shaping begin with reinforcing any response that resembles the desired behavior. For example, parents might praise a child for picking up one toy. As...
284
Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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Steps in the Modeling Process01:14

Steps in the Modeling Process

194
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
194
Reinforcement Schedules01:24

Reinforcement Schedules

139
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,...
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Instinctive Drift01:05

Instinctive Drift

200
Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
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Related Experiment Video

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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Guided Cooperation in Hierarchical Reinforcement Learning via Model-Based Rollout.

Haoran Wang, Zeshen Tang, Yaoru Sun

    IEEE Transactions on Neural Networks and Learning Systems
    |August 12, 2024
    PubMed
    Summary

    This study introduces Guided Cooperation via Model-Based Rollout (GCMR), a new framework for goal-conditioned hierarchical reinforcement learning (HRL). GCMR enhances interlevel cooperation and policy improvement in complex tasks by improving information synchronization and using model-based rollouts.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Robotics

    Background:

    • Goal-conditioned hierarchical reinforcement learning (HRL) uses temporal abstraction for exploration in complex, long-horizon tasks.
    • Effective interlevel communication and coordination are crucial for stable policy improvement in hierarchical systems.
    • Existing HRL algorithms often neglect interlevel cooperation, focusing primarily on subgoal discovery.

    Purpose of the Study:

    • To propose a novel goal-conditioned HRL framework, Guided Cooperation via Model-Based Rollout (GCMR), to enhance interlevel information synchronization and cooperation.
    • To improve sample efficiency and policy stability in complex reinforcement learning tasks.
    • To bridge the gap between subgoal discovery and interlevel cooperation in HRL.

    Main Methods:

    • Mitigation of state-transition errors in off-policy correction using model-based rollouts to enhance sample efficiency.
    • Constraining lower-level Q-function gradients with a model-inferred upper bound to stabilize exploration.
    • Implementing one-step rollout-based planning with higher-level critics guiding lower-level policies for global information transmission.

    Main Results:

    • The GCMR framework, when integrated with adjacency constraint and landmark-guided planning (ACLG), demonstrated more stable and robust policy improvement.
    • GCMR significantly outperformed various baseline algorithms and previous state-of-the-art (SOTA) methods.
    • Experimental results validated the effectiveness of GCMR's components in facilitating interlevel cooperation.

    Conclusions:

    • GCMR effectively enhances interlevel cooperation in goal-conditioned HRL by improving information synchronization and utilizing model-based rollouts.
    • The proposed framework leads to more stable and robust policy improvement, outperforming existing SOTA algorithms.
    • GCMR offers a promising direction for advancing exploration and performance in complex reinforcement learning tasks.