multi-objective

There are 54 repositories under multi-objective topic.

  • LucasAlegre/morl-baselines

    Multi-Objective Reinforcement Learning algorithms implementations.

    Language:Python45056984
  • MO-Gymnasium

    Farama-Foundation/MO-Gymnasium

    Multi-objective Gymnasium environments for reinforcement learning

    Language:Python35072551

  • dietmarwo/fast-cma-es

    A Python 3 gradient-free optimization library

    Language:C++14722120
  • AutoOED

    yunshengtian/AutoOED

    AutoOED: Automated Optimal Experimental Design Platform

    Language:Python1455920

  • mit-gfx/PGMORL

    [ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

    Language:Python12217335

  • yconst/YCML

    A Machine Learning and Optimization framework for Objective-C and Swift (MacOS and iOS)

    Language:Objective-C116181120

  • yunshengtian/DGEMO

    [NeurIPS 2020] Diversity-Guided Efficient Multi-Objective Optimization With Batch Evaluations

    Language:Python1085826

  • nathanrooy/landscapes

    A dependency free library of standardized optimization test functions written in pure Python.

    Language:Python622113

  • EmilioSchi/Niched-Pareto-Genetic-Algorithm-NPGA

    Genetic Algorithm (GA) for a Multi-objective Optimization Problem (MOP)

    Language:Python35304

  • moead-framework/framework

    MOEA/D is a general-purpose algorithm framework. It decomposes a multi-objective optimization problem into a number of single-objective optimization sub-problems and then uses a search heuristic to optimize these sub-problems simultaneously and cooperatively.

    Language:Python281418

  • RealVNF/DeepCoord

    DeepCoord: Self-Learning Network and Service Coordination Using Deep Reinforcement Learning

    Language:Python2721015

  • awilliea/Risk-based_RL_for_Optimal_Trading_Execution

    Language:Python19109

  • jbussemaker/SBArchOpt

    Surrogate-Based Architecture Optimization toolbox

    Language:Python16274

  • biological-alignment-benchmarks/ai-safety-gridworlds

    Extended, multi-agent and multi-objective (MaMoRL / MoMaRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.

    Language:Python112

  • fazaghifari/BayesianOptTools

    Bayesian Optimization and Uncertainty Analyses Tools

    Language:Python10107

  • paxplot/paxplot

    Paxplot is a Python visualization library for parallel axis, or parallel coordinate, plots.

    Language:Python101

  • HunterResearch/PyMOSO

    Python Multi-Objective Simulation Optimization: a package for using, implementing, and testing simulation optimization algorithms.

    Language:Python9202

  • LucasAlegre/sfols

    Code for the paper Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer - ICML 2022

    Language:Python8103

  • Haimrich/moham

    Multi-Objective Hardware-Mapping Co-Optimisation for Multi-DNN Workloads on Chiplet-based Accelerators

    Language:C++7211

  • wilrop/moqups

    An algorithm to calculate all pure strategy Nash equilibria in multi-objective games with quasiconvex utility functions

    Language:Python7201

  • dietmarwo/fcmaes-java

    A Java gradient-free optimization library

    Language:C++6122

  • hustnn/GeoSpark

    Multi-objective Optimization of Apache Spark in Geo-distributed Datacenters

    Language:Scala6302

  • relund/gMOIP

    2D and 3D plots of linear/integer programming models in R

    Language:HTML6282

  • amirbalef/PS_MOMAB

    Multi-Objective Multi-Armed Bandit

    Language:Python5102

  • bala4krish/Multi-Objective-Design-using-SQP

    Multi-Objective Design using Sequential Quadratic programming

    Language:MATLAB5102

  • biological-alignment-benchmarks/biological-alignment-gridworlds-benchmarks

    Safety challenges for AI agents' ability to learn and act in desired ways in relation to biologically and economically relevant aspects. The benchmarks are implemented in a gridworld-based environment. The environments are relatively simple, just as much complexity is added as is necessary to illustrate the relevant safety and performance aspects.

    Language:Python52

  • timm/ezr

    Explanation system for semi-supervised multi-objective optimization

    Language:Python522552

  • MajidAbdolshah/PRaDA_BO

    Multi-objective Bayesian optimisation framework.

    Language:Python4102

  • thomasWeise/BBDOB_W_Model

    The W-Model, a tunable Black-Box Discrete Optimization Benchmarking (BB-DOB) problem, implemented for the BB-DOB@GECCO Workshop.

    Language:Java4203

  • wilrop/ramo

    Algorithms for computing or learning equilibria in multi-objective games

    Language:Python4200

  • baiyan89/Line_Spectral_Estimation

    Code for Paper "Gridless Evolutionary Approach for Line Spectral Estimation With Unknown Model Order"

    Language:MATLAB3100

  • biological-alignment-benchmarks/bioblue

    Notable runaway-optimiser-like LLM failure modes on Biologically and Economically aligned AI safety benchmarks for LLM-s with simplified observation format. The benchmark themes include multi-objective homeostasis, (multi-objective) diminishing returns, complementary goods, sustainability, multi-agent resource sharing.

    Language:Python3102

  • Haimrich/medea

    MEDEA: A Multi-objective Evolutionary Approach to DNN Hardware Mapping

    Language:C++3110

  • kofgokhan/Bensolve.jl

    A Julia wrapper for Bensolve

    Language:Julia2

  • lamda-bbo/BPODC

    Official implementation of PPSN'24 paper "Biased Pareto Optimization for Subset Selection with Dynamic Cost Constraints"

    Language:Python220

  • Saeednikdel/learning-aided-evolution-for-optimization

    single & multi objective optimiztion

    Language:Jupyter Notebook2100