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

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.7K
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...
5.7K
Observational Learning01:12

Observational Learning

1.3K
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...
1.3K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

7.4K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
7.4K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

716
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
716
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

508
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...
508
Associative Learning01:27

Associative Learning

2.0K
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...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Spatial protection of cooperation by voting-based allocation.

Chaos (Woodbury, N.Y.)·2026
Same author

LLM agents overcome the machine penalty when acting fairly but not when acting selfishly or altruistically.

National science review·2026
Same author

Impacts of reinforcement learning-driven subsidy policies on evolutionary vaccination dynamics.

Chaos (Woodbury, N.Y.)·2026
Same author

Fitness-driven adaptive competition as a double-edged mechanism in maintaining biodiversity under cyclic competition.

Chaos (Woodbury, N.Y.)·2026
Same author

Social networking agency and prosociality are inextricably linked in economic games.

Nature human behaviour·2025
Same author

Effects of symmetric and asymmetric habitat loss on species coexistence in cyclic competition.

Chaos (Woodbury, N.Y.)·2025
Same journal

Exact computation of Lyapunov exponents via system parameters in multi-triangle chaotic maps: Bifurcation analysis and circuit realization.

Chaos (Woodbury, N.Y.)·2026
Same journal

Integrating score-based generative modeling and neural ODEs for accurate representation of multiscale chaotic dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A data-driven tuberculosis model with behavioral changes and saturated treatment: Optimal control and cost-effectiveness study.

Chaos (Woodbury, N.Y.)·2026
Same journal

Breathers, rational solutions, and their exact physical spectra in F = 1 spinor Bose-Einstein condensates.

Chaos (Woodbury, N.Y.)·2026
Same journal

Finite invariant sets with bridging points in logistic IFS.

Chaos (Woodbury, N.Y.)·2026
Same journal

Reputation-gated funding sustains cooperation: A spatial production game with selective investment.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Videos

Memory length and space shape multi-agent Q-learning dynamics.

Wei Wang1, Xiaogang Li1, Yongjuan Ma1

  • 1School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China.

Chaos (Woodbury, N.Y.)
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

Reinforcement learning players with memory-n strategies show higher cooperation than reactive-n players. Adjusting player information is key to fostering cooperation in multi-agent systems.

Related Experiment Videos

Area of Science:

  • Behavioral Economics
  • Game Theory
  • Artificial Intelligence

Background:

  • In repeated interactions, players adapt strategies based on past moves, necessitating complex cognitive abilities with increased memory.
  • Social learning through strategy imitation doesn't guarantee improved payoffs.
  • Reinforcement learning models human learning via past experiences, independent of co-players' strategies.

Purpose of the Study:

  • To investigate the impact of different memory lengths and spaces (memory-n, reactive-n, reactive-n counting) on cooperation evolution in reinforcement learning agents.
  • To determine how memory characteristics influence cooperative behavior and strategy effectiveness.

Main Methods:

  • Simulations of multi-agent systems using reinforcement learning agents with varying memory structures.
  • Analysis of cooperation levels and strategy evolution under different memory conditions (memory-n, reactive-n, reactive-n counting).

Main Results:

  • Memory-n players exhibited higher cooperation rates compared to reactive-n players.
  • Increased memory length promoted cooperation in memory-n agents but hindered it in reactive-n agents.
  • Reactive-n counting agents demonstrated memory compression, mitigating negative effects of excessive memory.

Conclusions:

  • Mutual cooperation and retaliation strategies are crucial for maintaining cooperation in reinforcement learning agents.
  • Optimizing the information available to players is essential for promoting cooperation in multi-agent systems.