.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/example_mountain_car.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_example_mountain_car.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_example_mountain_car.py:


Example: Solving an OpenAI Gym environment with CGP.
====================================================

This examples demonstrates how to solve an OpenAI Gym environment
(https://gym.openai.com/envs/) with Cartesian genetic programming. We
choose the "MountainCarContinuous" environment due to its continuous
observation and action spaces.

Preparatory steps:
Install the OpenAI Gym package: `pip install gym`

.. GENERATED FROM PYTHON SOURCE LINES 14-47

.. code-block:: default


    # The docopt str is added explicitly to ensure compatibility with
    # sphinx-gallery.
    docopt_str = """
      Usage:
        example_parametrized_nodes.py [--max-generations=<N>] [--visualize-final-champion]

      Options:
        -h --help
        --max-generations=<N>  Maximum number of generations [default: 1500]
        --visualize-final-champion  Create animation of final champion in the mountain car env.
    """

    import functools
    import warnings

    import matplotlib.pyplot as plt
    import numpy as np
    import sympy
    from docopt import docopt

    import cgp

    try:
        import gym
    except ImportError:
        raise ImportError(
            "Failed to import the OpenAI Gym package. Please install it via `pip install gym`."
        )


    args = docopt(docopt_str)








.. GENERATED FROM PYTHON SOURCE LINES 48-51

For more flexibility in the evolved expressions, we define two
constants that can be used in the expressions, with values 0.1 and
10.

.. GENERATED FROM PYTHON SOURCE LINES 51-61

.. code-block:: default



    class ConstantFloatZeroPointOne(cgp.ConstantFloat):
        _def_output = "0.1"


    class ConstantFloatTen(cgp.ConstantFloat):
        _def_output = "10.0"









.. GENERATED FROM PYTHON SOURCE LINES 62-68

Then we define the objective function for the evolution.  The inner
objective accepts a Python callable as input. This callable
determines the action taken by the agent upon receiving observations
from the environment. The fitness of the given callable on the task
is then computed as the cumulative reward over a fixed number of
episodes.

.. GENERATED FROM PYTHON SOURCE LINES 68-100

.. code-block:: default



    def inner_objective(f, seed, n_runs_per_individual, n_total_steps, *, render):

        env = gym.make("MountainCarContinuous-v0")

        env.seed(seed)

        cum_reward_all_episodes = []
        for _ in range(n_runs_per_individual):
            observation = env.reset()

            cum_reward_this_episode = 0
            for _ in range(n_total_steps):

                if render:
                    env.render()

                continuous_action = f(*observation)
                observation, reward, done, _ = env.step([continuous_action])
                cum_reward_this_episode += reward

                if done:
                    cum_reward_all_episodes.append(cum_reward_this_episode)
                    cum_reward_this_episode = 0
                    observation = env.reset()

        env.close()

        return cum_reward_all_episodes









.. GENERATED FROM PYTHON SOURCE LINES 101-105

The objective then takes an individual, evaluates the inner
objective, and updates the fitness of the individual. If the
expression of the individual leads to a division by zero, this error
is caught and the individual gets a fitness of -infinity assigned.

.. GENERATED FROM PYTHON SOURCE LINES 105-136

.. code-block:: default



    def objective(ind, seed, n_runs_per_individual, n_total_steps):

        if not ind.fitness_is_None():
            return ind

        f = ind.to_func()
        try:
            with warnings.catch_warnings():  # ignore warnings due to zero division
                warnings.filterwarnings(
                    "ignore", message="divide by zero encountered in double_scalars"
                )
                warnings.filterwarnings(
                    "ignore", message="invalid value encountered in double_scalars"
                )
                cum_reward_all_episodes = inner_objective(
                    f, seed, n_runs_per_individual, n_total_steps, render=False
                )

            # more episodes are better, more reward is better
            n_episodes = float(len(cum_reward_all_episodes))
            mean_cum_reward = np.mean(cum_reward_all_episodes)
            ind.fitness = n_episodes / n_runs_per_individual + mean_cum_reward

        except ZeroDivisionError:
            ind.fitness = -np.inf

        return ind









.. GENERATED FROM PYTHON SOURCE LINES 137-144

We then define the main loop for the evolution, which consists of:

- parameters for the population, the genome of individuals, and the evolutionary algorithm.
- creating a Population instance and instantiating the evolutionary algorithm.
- defining a recording callback closure for bookkeeping of the progression of the evolution.

Finally, we call the `evolve` method to perform the evolutionary search.

.. GENERATED FROM PYTHON SOURCE LINES 144-200

.. code-block:: default



    def evolve(seed):

        objective_params = {"n_runs_per_individual": 3, "n_total_steps": 2000}

        population_params = {"n_parents": 1, "seed": seed}

        genome_params = {
            "n_inputs": 2,
            "n_outputs": 1,
            "n_columns": 16,
            "n_rows": 1,
            "levels_back": None,
            "primitives": (
                cgp.Add,
                cgp.Sub,
                cgp.Mul,
                cgp.Div,
                cgp.ConstantFloat,
                ConstantFloatZeroPointOne,
                ConstantFloatTen,
            ),
        }

        ea_params = {"n_offsprings": 4, "tournament_size": 1, "mutation_rate": 0.04, "n_processes": 4}

        evolve_params = {
            "max_generations": int(args["--max-generations"]),
            "termination_fitness": 100.0,
        }

        pop = cgp.Population(**population_params, genome_params=genome_params)

        ea = cgp.ea.MuPlusLambda(**ea_params)

        history = {}
        history["expr_champion"] = []
        history["fitness_champion"] = []

        def recording_callback(pop):
            history["expr_champion"].append(pop.champion.to_sympy())
            history["fitness_champion"].append(pop.champion.fitness)

        obj = functools.partial(
            objective,
            seed=seed,
            n_runs_per_individual=objective_params["n_runs_per_individual"],
            n_total_steps=objective_params["n_total_steps"],
        )

        cgp.evolve(pop, obj, ea, **evolve_params, print_progress=True, callback=recording_callback)

        return history, pop.champion









.. GENERATED FROM PYTHON SOURCE LINES 201-202

For visualization, we define a function to plot the fitness over generations.

.. GENERATED FROM PYTHON SOURCE LINES 202-214

.. code-block:: default



    def plot_fitness_over_generation_index(history):
        width = 6.0
        fig = plt.figure(figsize=(width, width / 1.618))
        ax = fig.add_axes([0.15, 0.15, 0.8, 0.8])
        ax.set_xlabel("Generation index")
        ax.set_ylabel("Fitness champion")
        ax.plot(history["fitness_champion"])
        fig.savefig("example_mountain_car.png", dpi=300)









.. GENERATED FROM PYTHON SOURCE LINES 215-219

We define a function that checks whether the best expression
fulfills the "solving criteria", i.e., average reward of at least
90.0 over 100 consecutive
trials. (https://github.com/openai/gym/wiki/Leaderboard#mountaincarcontinuous-v0)

.. GENERATED FROM PYTHON SOURCE LINES 219-255

.. code-block:: default



    def evaluate_champion(ind):

        env = gym.make("MountainCarContinuous-v0")

        env.seed(seed)
        observation = env.reset()

        f = ind.to_func()

        cum_reward_all_episodes = []
        cum_reward_this_episode = 0
        while len(cum_reward_all_episodes) < 100:

            continuous_action = f(*observation)
            observation, reward, done, _ = env.step([continuous_action])
            cum_reward_this_episode += reward

            if done:
                cum_reward_all_episodes.append(cum_reward_this_episode)
                cum_reward_this_episode = 0
                observation = env.reset()

        env.close()

        cum_reward_average = np.mean(cum_reward_all_episodes)
        print(f"average reward over 100 consecutive trials: {cum_reward_average:.05f}", end="")
        if cum_reward_average >= 90.0:
            print("-> environment solved!")
        else:
            print()

        return cum_reward_all_episodes









.. GENERATED FROM PYTHON SOURCE LINES 256-258

Furthermore, we define a function for visualizing the agent's behaviour for
each expression that increase over the currently best performing individual.

.. GENERATED FROM PYTHON SOURCE LINES 258-287

.. code-block:: default



    def visualize_behaviour_for_evolutionary_jumps(seed, history, only_final_solution=True):
        n_runs_per_individual = 1
        n_total_steps = 999

        max_fitness = -np.inf
        for i, fitness in enumerate(history["fitness_champion"]):

            if only_final_solution and i != (len(history["fitness_champion"]) - 1):
                continue

            if fitness > max_fitness:
                expr = history["expr_champion"][i][0]
                expr_str = str(expr).replace("x_0", "x").replace("x_1", "dx/dt")

                print(f'visualizing behaviour for expression "{expr_str}" (fitness: {fitness:.05f})')

                x_0, x_1 = sympy.symbols("x_0, x_1")
                f_lambdify = sympy.lambdify([x_0, x_1], expr)

                def f(x):
                    return [f_lambdify(x[0], x[1])]

                inner_objective(f, seed, n_runs_per_individual, n_total_steps, render=True)

                max_fitness = fitness









.. GENERATED FROM PYTHON SOURCE LINES 288-291

Finally, we execute the evolution and visualize the results.
To animate the behavior of the car for the found expression, uncomment
the last line of the example.

.. GENERATED FROM PYTHON SOURCE LINES 291-310

.. code-block:: default



    if __name__ == "__main__":

        seed = 818821

        print("starting evolution")
        history, champion = evolve(seed)
        print("evolution ended")

        max_fitness = history["fitness_champion"][-1]
        best_expr = history["expr_champion"][-1]
        best_expr_str = str(best_expr).replace("x_0", "x").replace("x_1", "dx/dt")
        print(f'solution with highest fitness: "{best_expr_str}" (fitness: {max_fitness:.05f})')

        plot_fitness_over_generation_index(history)
        evaluate_champion(champion)
        if args["--visualize-final-champion"]:
            visualize_behaviour_for_evolutionary_jumps(seed, history)



.. image:: /auto_examples/images/sphx_glr_example_mountain_car_001.png
    :alt: example mountain car
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    starting evolution

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    [402/1500] max fitness: 2.0/opt/hostedtoolcache/Python/3.8.10/x64/lib/python3.8/site-packages/numpy/lib/function_base.py:3942: RuntimeWarning: invalid value encountered in multiply
      x2 = take(ap, indices_above, axis=axis) * weights_above

    [403/1500] max fitness: 2.0
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    [440/1500] max fitness: 102.55337700605139
    evolution ended
    solution with highest fitness: "9.0*dx/dt" (fitness: 102.55338)
    average reward over 100 consecutive trials: 96.41038-> environment solved!





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  18.822 seconds)


.. _sphx_glr_download_auto_examples_example_mountain_car.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: example_mountain_car.py <example_mountain_car.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: example_mountain_car.ipynb <example_mountain_car.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_