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Evolution Strategy Solver in Python

Project description

Evostra: Evolution Strategy for Python
--------

Evolutio 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

$ sudo python setup.py install


Install from PyPI:

.. code-block:: bash

$ sudo pip install evostra

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


Usage
--------

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 for builing the model and just pass the weights to ES.


Here we use Keras to build the model and we pass its weights to ES.

.. code:: python

from evostra import EvolutionStrategy
from keras.models import Model, Input
from keras.layers import Dense
from keras.optimizers import Adam # not important as there's no training here.
import numpy as np

input_layer = Input(shape=(5,1))
layer = Dense(8)(input_layer)
output_layer = Dense(3)(layer)
model = Model(input_layer, output_layer)
model.compile(Adam(), 'mse')




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]])
inp = np.expand_dims(inp, -1)

def get_reward(weights):
global solution, model, inp
model.set_weights(weights)
prediction = model.predict(inp)[0]
# 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

es = EvolutionStrategy(model.get_weights(), get_reward, population_size=50, sigma=0.1, learning_rate=0.001)
es.run(1000, print_step=100)


Here's the output:

.. code::

iter 0. reward: -68.819312
iter 100. reward: -0.218466
iter 200. reward: -0.110204
iter 300. reward: -0.089003
iter 400. reward: -0.078224
iter 500. reward: -0.063891
iter 600. reward: -0.049090
iter 700. reward: -0.027701
iter 800. reward: -0.013094
iter 900. reward: -0.009140


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

.. code:: python

optimized_weights = es.get_weights()
model.set_weights(optimized_weights)



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