Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
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Updated
Mar 5, 2025 - Python
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
GPU-accelerated NeuroEvolution of Augmenting Topologies (NEAT)
a modular reinforcement learning library with JAX agents
MetaDE is a GPU-accelerated evolutionary framework that optimizes Differential Evolution (DE) strategies via meta-level evolution. Supporting both JAX and PyTorch, it dynamically adapts mutation and crossover strategies for efficient large-scale black-box optimization.
GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA.
Relentlessly learning, persistently failing, but never surrendering.
[ICML 2024] Official environments and JAX-implementations for "Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments"
Procedural Environment Generation for Accelerated Multi-Agent Reinforcement Learning
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