Examples

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:

cd evotorch
pip install jupyterlab
jupyter lab

In the examples/notebooks/ directory, you can find the following notebooks:

• Gym Experiments with PGPE and CoSyNE demonstrates how you can solve "LunarLanderContinuous-v2" using both PGPE and CoSyNE following the configurations described in the paper proposing ClipUp and the JMLR paper on the CoSyNE algorithm.
• Minimizing Lennard-Jones Atom Cluster Potentials recreates experiments from the paper introducing SNES, showing that the algorithm can effectively solve the challenging task of minimising Lennard-Jones atom cluster potentials.
• Model_Predictive_Control_with_CEM demonstrates the application of the Cross-Entropy Method CEM to Model Predictive Control (MPC) of the MuJoCo task named "Reacher-v4".
• Training MNIST30K recreates experiments from a recent paper which demonstrates that SNES can 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 SNES is 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 examples/scripts directory:

• rl.py re-implements almost all experiments from the paper proposing ClipUp, and is easily reconfigured to replicate any particular experiment using sacred.
• rl_enjoy.py allows you to easily visualize and enjoy agents trained through rl.py.
• tinytraj_humanoid_bullet.py implements the modified "pybullet_envs:HumanoidBulletEnv-v0" environment from the paper.