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Example Training and Submission
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In the folder ``rrc_simulation/example`` you can find a full example of a training 
method and how to create a submission using the trained policy:

    -   ``example_pushing_training_env.py`` is a gym environment adapted
        from the standard ``cube_env.py``. It is adapted to facilitate training.
        Note however, that care has been taken to not modify the simulation itself,
        such that state-action trajectories remain coherent with ``cube_env.py``.
    

    -   ``train_pushing_ppo.py`` trains a pushing policy on the env defined above,
        using PPO.

    -   ``view_pushing_ppo.py`` loads a trained policy and visualizes it. For
        convenience, the repository already contains some trained policies in 
        the folder ``training_checkpoints``. You may for instance visualize 
        a policy by executing::

            python view_pushing_ppo.py --model_path ./training_checkpoints/ --time_steps 78000000

    -   ``rrc_simulation/example/evaluate_policy.py`` was adapted from
        ``rrc_simulation/scripts/evaluate_policy.py``. This file allows you to run the full
        evaluation on
        the trained policy by executing::

            rrc_evaluate path/to/output_directory

        in the ``rrc_simulation/example`` folder. Note that on our side, we will
        run ``rrc_evaluate`` at the default location of the
        ``evaluate_policy.py`` which is the ``scripts`` folder.  So if you
        wanted to use this example as an actual submission, you would replace
        ``rrc_simulation/scripts/evaluate_policy.py`` with
        ``rrc_simulation/example/evaluate_policy.py``.