Mars Simulation Project Official Codebase
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Updated
Jun 10, 2024 - Java
Mars Simulation Project Official Codebase
Main repository for developing the 2024+ versions of GAMA
Exploring the effect of hospital capacity and various parameter sweeps on patterns of epidemiological spread (based on the SIR model)
This extension aims to allow agent-based models to account for norms. During plan generation, agents must be able to represent and reason about norms. The end-goal is to be able to describe a planning problem with norms endowed by its organizations through the various roles that the agent must fulfill, and see how it affects its plans.
SARL Agent-Oriented Programming Language http://www.sarl.io
Main repository for developing the 1.x versions of GAMA
A simple framework for multi-agent simulation in java
Similar2Logo is a Logo-like multiagent-based simulation environment based on the SIMILAR API.
Hybrid Aggregated Agent‐based Microsimulation of Segregation
Passenger flow simulation framework for advanced aircraft cabin layouts. Written in Java. In development at Bauhaus Luftfahrt e.V. since 2014.
supply-demand and electricity market model
JASA is a high-performance auction simulator written in JAVA. It is designed for performing experiments in agent-based computational economics.
Java Agent Based Modelling toolkit
Implemented a player agent that uses algorithmic strategy to score high on a predefined, random seeded Pac-Man game framework.
Multi Agent Transport Simulator
Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
DEPONS model: Simulating effects of disturbances on the North Sea harbour porpoise population
Agent-based Model (ABM) using MASON for land use and policy simulations
Code for my bachelor thesis: "Evolutionary language competition - an agent-based model"
this project enhances the MCTS agent in Pommerman with three types of AMAF heuristics. Comparisons by win rate against other agents are made among the three, and we also tune the parameters including ɑ, C (in the UCB1 function) and rollout depth to further improve win rate.
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