Note that there is a significant amount of duplication between these scripts, and this is intentional. The goal is to make it easier to see what the example is doing, without making the user dig through a bunch of utility code that is not directly related to the NEAT library usage.

Documentation for the library and some of the examples may be found here.

For convenience, a conda environment that supports running all of the examples is available in this directory (see environment.yml).

• xor A "hello world" sample showing basic usage on the 2-input XOR problem.

• memory-fixed Reproduce a fixed-length sequence of binary inputs (currently experimental / not working as intended).

• memory-variable Reproduce a variable-length sequence of binary inputs (currently experimental / not working as intended).

• neuron-demo Plot outputs of very simple CTRNN and Izhikevich neuron models.

• single-pole-balancing Balance a pole on top of a movable cart and optionally render videos of successful policies.

• picture2d Generate 2D color or grayscale images using Compositional Pattern-Producing Networks, including an interactive picture-breeder style demo.

• lunar-lander Solve the Gymnasium LunarLander-v3 environment with a modern API-compatible example.

• bipedal-walker Control the Gymnasium BipedalWalker-v3 environment using a continuous-action NEAT policy.

• inverted-double-pendulum Evolve a controller for the Gymnasium InvertedDoublePendulum-v5 environment using a MuJoCo-based simulator.

• hopper Evolve a controller for the Gymnasium Hopper-v5 environment using a MuJoCo-based continuous-control task.

• parallel-reproducible Demonstrate how to use ParallelEvaluator with deterministic seeding to get reproducible parallel evolution runs.

• export Train a small XOR network and export the resulting NEAT network to a framework-agnostic JSON format.

• lorenz-ctrnn Evolve CTRNN networks to predict the Lorenz attractor, demonstrating per-node evolvable time constants (v2.0 feature).

• signal-tracking-gpu GPU-accelerated CTRNN signal tracking comparison (requires neat-python[gpu]).

• spike-timing-gpu GPU-accelerated Izhikevich spiking network spike timing comparison (requires neat-python[gpu]).

• openai-lander Archived OpenAI Gym LunarLander example containing pre-trained winner genomes referenced in the documentation.