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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:

Timing and Consequences on Behavior

Timing and Consequences on Behavior

In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective.
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant...

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria 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...

Cognitive Learning

Cognitive Learning

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.
Tolman introduced the idea that behavior is influenced by...

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Related Experiment Video

Updated: Aug 11, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Safe reinforcement learning under temporal logic with reward design and quantum action selection.

Mingyu Cai¹, Shaoping Xiao², Junchao Li³

¹Department of Mechanical Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA, 18015, USA.

Scientific Reports

|February 2, 2023

Summary

This study introduces advanced Reinforcement Learning (RL) with safety values and quantum action selection. The novel method enhances task completion probability and reduces unsafe state visits during training.

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Area of Science:

Artificial Intelligence
Machine Learning
Quantum Computing

Background:

Reinforcement Learning (RL) faces challenges with unsafe states and sparse rewards, limiting real-world applications.
Complex task specifications are often defined using formal languages like Linear Temporal Logic (LTL).

Purpose of the Study:

To develop an advanced RL method addressing safety, sparse rewards, and complex task satisfaction.
To improve the efficiency and reliability of RL agents in dynamic environments.

Main Methods:

Incorporation of safety value functions for enhanced training safety.
Utilization of Embedded Limit-Deterministic Generalized Büchi Automaton (E-LDGBA) for LTL formula representation.
Development of an automaton-based reward system and reward shaping process.
Introduction of a quantum-inspired action selection algorithm for exploration-exploitation balance.

Main Results:

The proposed method synthesizes finite policies maximizing task satisfaction probability.
Theoretical analysis confirms optimal policy adherence for task accomplishment.
Demonstrated reduction in visits to unsafe states during simulations.
Effective convergence to optimal policies is achieved.

Conclusions:

The advanced RL framework effectively handles complex tasks while prioritizing safety.
The integration of quantum-inspired algorithms and formal methods offers a robust solution for RL challenges.
The method shows significant performance improvements in reducing unsafe states and ensuring policy convergence.