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Monte Carlo Tree Search for Gym Style Environments
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Monte Carlo Tree Search for Gym Style Environments

Contents:

  • Visualizations
  • Example Gallery
    • Frozenlake Gallery
      • FrozenLake 4x4
      • FrozenLake 4x4 (Action History)
      • MCTS Tree Visualisation
      • FrozenLake 6x6
      • Frozenlake Visualisation
    • Cartpole Gallery
      • Cartpole-v1
    • Job Shop Gallery
      • graph-jsp-env (Deepcopy Wrapper)
      • graph-jsp-env (Action History Wrapper)
      • graph-jsp-env (Custom Wrapper)
      • graph-matrix-jsp-env (Deepcopy Wrapper)
      • graph-matrix-jsp-env (Action History Wrapper)
      • graph-matrix-jsp-env (Custom Wrapper)
      • JSSEnv (Action History Wrapper)
      • JSSEnv (Deepcopy Wrapper)
      • gnn_jsp_env (Deepcopy Wrapper)
      • gnn_jsp_env (Action History Wrapper)
      • minimal-jsp-env (Action History Wrapper)
      • minimal-jsp-env (Deepcopy Wrapper)
  • Source Code
    • GymctsABC Wrapper
    • DeepCopy Wrapper
    • Action History Wrapper
    • MCTS Tree Components
    • Tree Visualization
    • Colorful String Utilities
  • Contributing
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Frozenlake GalleryΒΆ

This section contains a collection of examples that demonstrate how to use the library with the FrozenLake environment. The FrozenLake environment is a classic reinforcement learning environment that is often used to test and benchmark reinforcement learning algorithms. It may not be the best fit for a MCTS approach, but a lot of researches are familiar with it and it is a good starting point for getting to know the library.

Note

Please note that you have to install the gymnasium library in order to run the examples. You can do this by running the following command:

pip install gymnasium
  • FrozenLake 4x4
  • FrozenLake 4x4 (Action History)
  • MCTS Tree Visualisation
  • FrozenLake 6x6
  • Frozenlake Visualisation
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FrozenLake 4x4
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Example Gallery
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