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Reinforcement Schedules

Reinforcement Schedules

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,...

Reinforcement

Reinforcement

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:

Associative Learning

Associative Learning

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...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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 of...

Heuristics

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...

Observational Learning

Observational Learning

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 because...

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Related Experiment Video

Updated: May 10, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Heuristically-accelerated multiagent reinforcement learning.

Reinaldo A C Bianchi, Murilo F Martins, Carlos H C Ribeiro

IEEE Transactions on Cybernetics

|June 13, 2013

Summary

Heuristically-Accelerated Multiagent Reinforcement Learning (HAMRL) uses heuristics to speed up multiagent reinforcement learning (RL) algorithms. This novel approach significantly improves performance and action selection policies in adversarial domains.

Related Experiment Videos

Last Updated: May 10, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

Artificial Intelligence
Machine Learning
Reinforcement Learning

Background:

Multiagent reinforcement learning (RL) algorithms face challenges in convergence speed and efficiency.
Existing RL methods can be computationally intensive, especially in complex, adversarial environments.

Purpose of the Study:

Introduce a novel class of algorithms, Heuristically-Accelerated Multiagent Reinforcement Learning (HAMRL).
Enhance the efficiency and performance of established multiagent RL algorithms using heuristic guidance.

Main Methods:

Developed HAMRL algorithms integrating heuristic functions into existing RL frameworks (e.g., Minimax-Q).
Conducted theoretical convergence analysis for four HAMRL variants (HAMMQ, HAMQ(λ), HAMQS, HAMS).
Performed systematic evaluations in two distinct adversarial domains.

Main Results:

Demonstrated that HAMRL algorithms significantly accelerate learning compared to vanilla RL.
Showcased that even simple heuristics yield virtually optimal action selection policies.
Achieved substantial performance improvements in adversarial settings.

Conclusions:

HAMRL provides a robust framework for accelerating multiagent reinforcement learning.
The integration of heuristics offers a practical method to improve RL efficiency and effectiveness.
This approach holds promise for applications requiring rapid decision-making in complex environments.