Multi-Objective Reinforcement Learning algorithms implementations.

Multi-objective Gymnasium environments for reinforcement learning

A Python 3 gradient-free optimization library

AutoOED: Automated Optimal Experimental Design Platform

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

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

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

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

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

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.

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

Surrogate-Based Architecture Optimization toolbox

Extended, multi-agent, and multi-objective (MaMoRL / MoMaRL) gridworld environments building framework 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.

Bayesian Optimization and Uncertainty Analyses Tools

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

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

Safety challenges for RL and LLM agents' ability to learn and use biologically and economically aligned utility functions. 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.

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

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