We provide a number of reference examples directly on our GitHub. Each of these examples demonstrates how to recreate a particular experiment or result from recent evolutionary algorithm literature, to highlight that Evotorch is highly suited to both academic research in and advanced industrial applications of evolutionary algorithms.
We provide a number of examples as jupyter notebooks. The easiest way to get started with these examples is to run:
- Gym Experiments with PGPE and CoSyNE
- Minimizing Lennard-Jones Atom Cluster Potentials
Demonstrates the application of the Cross-Entropy Method
CEMto Model Predictive Control (MPC) of the MuJoCo task named "Reacher-v4".
- Training MNIST30K
Recreates experiments from a recent paper which demonstrates that
SNEScan be used to solve supervised learning problems. The script in particular recreates the training of the 30K-parameter 'MNIST30K' model on the MNIST dataset, but can easily be reconfigured to recreate other experiments from that paper.
- Variational Quantum Eigensolvers with SNES
Re-implements (with some minor changes in experimental setup), experiments in a recent paper demonstrating that
SNESis a scalable alternative to analytic gradients on a quantum computer, and can practically optimize Quantum Eigensolvers.
In addition, to help you to implement advanced neuroevolutionary reinforcement learning settings, we have provided 3 python scripts in the
Demonstrates single objective black-box optimization using a distribution-based algorithm, accelerated using vectorization on a single GPU/CPU.
Demonstrates multi-objective optimization using parallelization on all CPU cores without vectorization.
Allows you to easily visualize and enjoy agents trained through
Demonstrates how to solve a simple Gym problem using the PGPE algorithm and ClipUp optimizer.