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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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Reinforcement Schedules01:24

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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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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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Updated: Dec 25, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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Restoring chaos using deep reinforcement learning.

Sumit Vashishtha1, Siddhartha Verma1

  • 1Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, Florida 33431, USA.

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|April 3, 2020
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Summary
This summary is machine-generated.

Deep Reinforcement Learning (RL) prevents undesirable non-chaotic states in non-linear dynamical systems. The RL agent learns to sustain chaotic trajectories in the Lorenz system, demonstrating a novel control strategy.

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Area of Science:

  • Non-linear dynamics
  • Chaos theory
  • Artificial intelligence

Background:

  • Catastrophic bifurcations, or crises, in non-linear systems can lead to undesirable non-chaotic states after chaotic transients.
  • Controlling these crises and maintaining chaotic behavior has been a significant challenge in dynamical systems research.

Purpose of the Study:

  • To demonstrate the efficacy of deep Reinforcement Learning (RL) in restoring and sustaining chaos in a transiently chaotic regime.
  • To develop an RL-based strategy for controlling catastrophic bifurcations in non-linear systems without prior knowledge of their governing equations.

Main Methods:

  • Utilized a deep Reinforcement Learning (RL) agent to interact with the Lorenz system of equations.
  • Trained the RL agent to perturb system parameters to maintain chaotic trajectories.
  • Analyzed the RL agent's control decisions to derive a simplified control law.

Main Results:

  • The deep RL agent successfully restored chaos in the transiently chaotic regime of the Lorenz system.
  • The agent autonomously discovered an effective strategy for parameter perturbation without needing knowledge of the Lorenz equations.
  • A simple control law derived from the agent's decisions was implemented and validated for sustaining chaos.

Conclusions:

  • Deep Reinforcement Learning (RL) offers a powerful and adaptive approach for controlling catastrophes in non-linear dynamical systems.
  • This study highlights the potential of AI in managing complex system behaviors and preventing undesirable state transitions.