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Related Concept Videos

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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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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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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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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.
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Movement Retraining using Real-time Feedback of Performance
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Near real-time online reinforcement learning with synchronous or asynchronous updates.

Mircea-Bogdan Radac1, Darius-Pavel Chirla2

  • 1Department of Automation and Applied Informatics, Politehnica University of Timisoara, Bvd. V. Parvan, 2, 300223, Timisoara, Romania. mircea.radac@upt.ro.

Scientific Reports
|May 17, 2025
PubMed
Summary

This study introduces an online Reinforcement Learning (RL) method for complex system control, enabling near real-time learning by interleaving system interaction and neural network training. The approach is validated through simulations and hardware experiments, showing promise for practical applications.

Keywords:
Learning systemNeural networksOnline learningReal-timeReinforcement learning

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Area of Science:

  • Control Systems Engineering
  • Machine Learning
  • Artificial Intelligence

Background:

  • Reinforcement Learning (RL) is effective for controlling complex systems but faces limitations in practical applications due to challenges in interleaving learning and interaction.
  • Integrating neural network complexity with real-time learning capabilities remains a significant hurdle for widespread RL adoption in control systems.

Purpose of the Study:

  • To propose and validate an online Reinforcement Learning (RL) solution that addresses the limitations of interleaving environment interaction and learning steps.
  • To enable near real-time learning capabilities for complex and unknown dynamical systems using RL.

Main Methods:

  • An online learning solution is developed, encoding system states using past signals and reference model/input states.
  • Value function and controller neural networks are trained online via backpropagation using system interaction data.
  • The methodology is tested using a model-reference tracking control problem in both simulation and experimental hardware setups.

Main Results:

  • The proposed online RL methodology is demonstrated to be valid through simulation and experimental case studies.
  • Performance operation times were compared between synchronous and asynchronous updates using two high-level software packages.
  • Analysis of software challenges and code runtime numbers provides insights into practical implementation.

Conclusions:

  • Online synchronous RL shows strong potential for lower-order systems with fast dynamics, aligning with real-time requirements.
  • Asynchronous online RL facilitates scaling to higher-dimensional systems and faster dynamics, even in non-hard real-time scenarios.