Multi-Objective Reinforcement Learning algorithms implementations.

Explanation system for semi-supervised multi-objective optimization

Multi-objective Gymnasium environments for reinforcement learning

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

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

A Python 3 gradient-free optimization library

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

AutoOED: Automated Optimal Experimental Design Platform

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

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

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.

Bayesian Optimization and Uncertainty Analyses Tools

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

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.

Surrogate-Based Architecture Optimization toolbox

A lean little learning library, in LUA.

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

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

Multi-objective Bayesian optimisation framework.