Evolution Strategy Solver in Python

## Project description

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

<https://github.com/alirezamika/flappybird-es>`_

`An AI agent learning to walk using evostra

<https://github.com/alirezamika/bipedal-es>`_

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

--------

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

<https://github.com/alirezamika/flappybird-es>`_

`An AI agent learning to walk using evostra

<https://github.com/alirezamika/bipedal-es>`_

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

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