Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

137
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...
137
Reinforcement Schedules01:24

Reinforcement Schedules

224
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,...
224
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Reinforcement01:23

Reinforcement

305
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:
305
Observational Learning01:12

Observational Learning

253
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...
253
Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

645
The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
645

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Smart strategies to navigate turbulent odor plumes reorienting to local wind.

ArXiv·2026
Same author

Policy heterogeneity improves collective olfactory search in three-dimensional turbulence.

Physical review. E·2026
Same author

Multiscale data assimilation in turbulent models.

Physical review. E·2026
Same author

TURB-Smoke. A database of Lagrangian pollutants emitted from point sources in turbulent flows with a mean wind.

Scientific data·2026
Same author

Bayesian network 3D event reconstruction in the Cygno optical TPC for dark matter direct detection.

The European physical journal. C, Particles and fields·2025
Same author

Defects, Parcellation, and Renormalized Negative Diffusivities in Nonhomogeneous Oscillatory Media.

Physical review letters·2025

Related Experiment Video

Updated: Aug 8, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

Taming Lagrangian chaos with multi-objective reinforcement learning.

Chiara Calascibetta1, Luca Biferale2, Francesco Borra3

  • 1Department of Physics & INFN, University of Rome 'Tor Vergata', Via della Ricerca Scientifica 1, 00133, Rome, Italy. calascibetta@roma2.infn.it.

The European Physical Journal. E, Soft Matter
|March 3, 2023
PubMed
Summary
This summary is machine-generated.

Multi-objective reinforcement learning (MORL) optimizes two active particles in 2D complex flows. MORL finds optimal trade-offs between dispersion and control costs, outperforming heuristic strategies.

More Related Videos

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.7K
Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.1K

Related Experiment Videos

Last Updated: Aug 8, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.7K
Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.1K

Area of Science:

  • Fluid Dynamics
  • Artificial Intelligence
  • Control Theory

Background:

  • Complex fluid flows present challenges for particle dispersion and control.
  • Optimizing multiple objectives simultaneously, such as minimizing dispersion and control costs, is crucial for active particle systems.

Purpose of the Study:

  • To apply multi-objective reinforcement learning (MORL) to optimize the behavior of two active particles in 2D complex flows.
  • To investigate the trade-offs between minimizing dispersion rate and control activation cost.
  • To analyze the impact of discrete decision times on control strategies.

Main Methods:

  • Utilized multi-objective reinforcement learning (MORL) by combining scalarization techniques with a Q-learning algorithm.
  • Modeled Lagrangian drifters with variable swimming velocity.
  • Evaluated performance against heuristic strategies under various discrete time intervals.

Main Results:

  • MORL successfully identified a Pareto frontier of optimal trade-off solutions.
  • MORL-derived strategies significantly outperformed benchmark heuristic strategies.
  • A specific range of discrete decision times was found where reinforcement learning strategies showed substantial improvement over heuristics.

Conclusions:

  • MORL provides an effective framework for optimizing multi-objective problems in complex fluid dynamics.
  • The choice of decision time significantly impacts the effectiveness of control strategies, with larger times requiring more flow knowledge.
  • For smaller decision times, heuristic strategies can approach Pareto optimality.