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https://github.com/alirezamika/evostra

A fast Evolution Strategy implementation in Python
https://github.com/alirezamika/evostra

ai ai-learning artificial-intelligence deep-learning evolution-strategies evolutionary-algorithms machine-learning python reinforcement-learning

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A fast Evolution Strategy implementation in Python

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README 

        
Evostra: Evolution Strategy for Python

--------



Evolution Strategy (ES) is an optimization technique based on ideas of adaptation and evolution.

You can learn more about it at https://blog.openai.com/evolution-strategies/



Installation

--------

It's compatible with both python2 and python3.



Install from source:



.. code-block:: bash



    $ python setup.py install



    

Install latest version from git repository using pip:



.. code-block:: bash



    $ pip install git+https://github.com/alirezamika/evostra.git

    

    

Install from PyPI:



.. code-block:: bash



    $ pip install evostra

    

(You may need to use python3 or pip3 for python3)



Sample Usages

--------



`An AI agent learning to play flappy bird using evostra 

`_



`An AI agent learning to walk using evostra 

`_



How to use

--------



The input weights of the EvolutionStrategy module is a list of arrays (one array with any shape for each layer of the neural network), so we can use any framework to build the model and just pass the weights to ES.



For example we can use Keras to build the model and pass its weights to ES, but here we use Evostra's built-in model FeedForwardNetwork which is much faster for our use case:



.. code:: python



    import numpy as np

    from evostra import EvolutionStrategy

    from evostra.models import FeedForwardNetwork



    # A feed forward neural network with input size of 5, two hidden layers of size 4 and output of size 3

    model = FeedForwardNetwork(layer_sizes=[5, 4, 4, 3])



Now we define our get_reward function:



.. code:: python



    solution = np.array([0.1, -0.4, 0.5])

    inp = np.asarray([1, 2, 3, 4, 5])



    def get_reward(weights):

        global solution, model, inp

        model.set_weights(weights)

        prediction = model.predict(inp)

        # here our best reward is zero

        reward = -np.sum(np.square(solution - prediction))

        return reward



Now we can build the EvolutionStrategy object and run it for some iterations:



.. code:: python



    # if your task is computationally expensive, you can use num_threads > 1 to use multiple processes;

    # if you set num_threads=-1, it will use number of cores available on the machine; Here we use 1 process as the

    #  task is not computationally expensive and using more processes would decrease the performance due to the IPC overhead.

    es = EvolutionStrategy(model.get_weights(), get_reward, population_size=20, sigma=0.1, learning_rate=0.03, decay=0.995, num_threads=1)

    es.run(1000, print_step=100)



Here's the output:



.. code::



    iter 100. reward: -68.819312

    iter 200. reward: -0.218466

    iter 300. reward: -0.110204

    iter 400. reward: -0.001901

    iter 500. reward: -0.000459

    iter 600. reward: -0.000287

    iter 700. reward: -0.000939

    iter 800. reward: -0.000504

    iter 900. reward: -0.000522

    iter 1000. reward: -0.000178

    

    

Now we have the optimized weights and we can update our model:



.. code:: python

    

    optimized_weights = es.get_weights()

    model.set_weights(optimized_weights)

    



Todo

--------

- Add distribution support over network