WorldDynamics.jl
Agents.jl
| WorldDynamics.jl | Agents.jl | |
|---|---|---|
| 1 | 13 | |
| 63 | 693 | |
| - | 1.9% | |
| 7.4 | 8.8 | |
| about 1 month ago | 8 days ago | |
| Julia | Julia | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
WorldDynamics.jl
Agents.jl
- Ask HN: I just want to have fun programming again
-
[P] Stochastic Differentiable Programming: Unbiased Automatic Differentiation for Discrete Stochastic Programs (such as particle filters, agent-based models, and more!)
We mean the standard "agent based model" https://www.pnas.org/doi/10.1073/pnas.082080899, https://en.wikipedia.org/wiki/Agent-based_model . The kind of thing you'd use Agents.jl for. For example, look at agent-based infection models. In these kind of models you create many individuals (agents) with rules. Each agent moves around, but if one is standing near an agent that is infected, there's a probability of infecting the nearby agent. What is the average percentage of infected people at time t?
-
What are the Netlogo competitors?
Jullia has packages too.
-
Julia ♥ Agent Based Modeling #2: Work, Eat, Trade, Repeat
Agent-based modeling looks like an interesting topic, something ripe for fun little side projects. The short (three paragraph) "Crash course on agent based modeling" [1] from the package docs gave me an idea of why ABM is useful, and scrolling through the example model [2] kinda answers what conveniences the package gives me over implementing the simulation myself.
Has anyone here used ABM for a serious project? Fields like economics and sociology are mentioned, but how prevalent is Agent based modeling in those fields in practice?
[1] https://juliadynamics.github.io/Agents.jl/stable/#Crash-cour...
-
Tetris game as Agent-Based modeling: maximizing density
Are the pieces the agents? I would recommend looking at Collaborative Diffusion for some examples of combining agent-based techniques with game modeling. As for frameworks, check out agentpy or Agents.jl for alternatives that are moreso software libraries that presume knowledge of programming.
-
What framework would you recommend to build a Tetris game AI using reinforcement learning?
I has a look to Julia too. There are nice tools build by JuliaDynamics. I.e. Agents.jl for agent based modeling. It handles collisions. There is also a framework for reinforcement learning. Also for Genetic Algorithms. Then I found a set of libraries related to Geometry. But it seems to be a lot of work to put that together for my use case.
- What would you like to see in a complex systems modeling software platform?
-
Transition from R Tidyverse to Julia (VS Code)
For agent based modelling, you've come to the right place because Agents.jl is great! It has a way to get interactive visualisations from your models, although I haven't used it myself. See this year's JuliaCon talk about Agents.jl to get an idea of what it can do.
-
Agent Based Simulation
I'm always happy to find you have documentation ;). The doc from https://github.com/JuliaDynamics/Agents.jl was pretty helpful to a noob like me.
-
"No backend available" error when using InteractiveDynamics
Here is the issue. Someone already commented saying it's due to a change in InteractiveDynamics.jl and referenced a pull request. I guess all we need to do is wait.
What are some alternatives?
SymbolicRegression.jl - Distributed High-Performance Symbolic Regression in Julia
Molly.jl - Molecular simulation in Julia
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
mesa - Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors.
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
LanguageServer.jl - An implementation of the Microsoft Language Server Protocol for the Julia language.
Catalyst.jl - Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
NetLogo - turtles, patches, and links for kids, teachers, and scientists
Optimization.jl - Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
ReinforcementLearning.jl - A reinforcement learning package for Julia
KernelAbstractions.jl - Heterogeneous programming in Julia