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This project solves Gym's Bipedal Walker problem using modified deep neuroevolution.

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standa42/bipedal-walker-deep-ga

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bipedal-walker-deep-ga

The project solves Bipedal-Walker-v3 using deep neural networks trained by genetic algorithm. We used simplified version of Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning. Our best model reached average return of ~310 over 100 episodes.

Model

How to install project

  1. Clone the repository and change current directory
git clone https://github.com/standa42/bipedal-walker-deep-ga
cd "bipedal-walker-deep-ga"
  1. Create virtual environment
  • you can also reuse an existing one but this tutorial acts as you would create a new one
/usr/bin/python3 -m venv "venv"
  1. Install pip requirements
venv/bin/python3 -m pip install -r "requirements.txt"

How to train a model

Training is simple using following script:

venv/bin/python3 train.py

Default parameters are already set for the best reached model. Logs are stored into logs/train{TIMESTAMP}_{UUID4_CODE} directory.

How to evaluate a model

Evaluation of model is done using following script:

venv/bin/python3 evaluate.py

Evaluation can be visualized using render_each parameter.

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This project solves Gym's Bipedal Walker problem using modified deep neuroevolution.

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