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hyperparams_vis_tool's Introduction

Visualization Tool for Intuitive Hyperparameter Optimization

InfoVis Final Project.

How to use

  1. execute backend server.

    • $ cd backend
      $ yarn start
  2. execute frontend server.

    • $ cd backend/frontend
      $ yarn start
  3. explore!

How to train RL and add data

requirements

  1. tensorflow==1.13.1
  2. sklearn
  3. gym
  4. mujoco-py
  5. matplotlib

train

  • $ cd SAC
    $ ./train.sh
  • After few minutes, the policy learning rate version is trained, and the visualization web is updated.

  • To get more information about train arguments, you can execute "python main.py -h".

hyperparams_vis_tool's People

Contributors

dobro12 avatar

Watchers

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