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https://github.com/nnaisense/evotorch

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.
https://github.com/nnaisense/evotorch

artificial-intelligence distributed evolutionary-algorithms evolutionary-computation gpu neural-networks neuroevolution optimization optimization-algorithms python pytorch reinforcement-learning

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Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.

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---



Welcome to the EvoTorch project!

EvoTorch is an open source evolutionary computation library developed at [NNAISENSE](https://nnaisense.com), built on top of [PyTorch](https://pytorch.org/).

See the [documentation](https://docs.evotorch.ai) for in-depth guidance about using EvoTorch, and [join us on Slack](https://join.slack.com/t/evotorch/shared_invite/zt-1hcj9prrl-wQBMX4JtaB6WdGKSDjZGXw) for discussions.



Get started by installing EvoTorch:

```

pip install evotorch

```



With EvoTorch, one can solve various optimization problems, regardless of whether they are differentiable (i.e. allow gradient descent). Among the problem types that are solvable with EvoTorch are:

- Black-box optimization problems (continuous or discrete)

- Reinforcement learning tasks

- Supervised learning tasks



Various evolutionary computation algorithms are available in EvoTorch:

- **Distribution-based search algorithms:**

    - **PGPE:** Policy Gradients with Parameter-based Exploration.

    - **XNES:** Exponential Natural Evolution Strategies.

    - **CMA-ES:** Covariance Matrix Adaptation Evolution Strategies.

    - **SNES:** Separable Natural Evolution Strategies.

    - **CEM:** Cross Entropy Method.

- **Population-based search algorithms:**

    - **GeneticAlgorithm:** A genetic algorithm implementation. Also supports multiple objectives, in which case it behaves like **NSGA-II**.

    - **CoSyNE:** Cooperative Synapse Neuroevolution.

    - **MAPElites:** Multi-dimensional Archive of Phenotypic Elites



Since all of these algorithms are implemented in PyTorch, they benefit from use of vectorization and parallelization on GPUs, drastically speeding up optimization when GPUs are available.

Using [Ray](https://github.com/ray-project/ray), EvoTorch scales these algorithms even further by splitting the workload across:

- multiple CPUs

- multiple GPUs

- multiple computers in a Ray cluster



# Examples



Below are some code examples that demonstrate the API of EvoTorch.



## A black-box optimization example



Any objective function defined to work with PyTorch can be used directly with EvoTorch.

A non-vectorized objective function simply receives a solution as a 1-dimensional torch tensor, and returns a fitness as a scalar.

A vectorized objective function receives a batch of solutions as a 2-dimensional torch tensor, and returns a 1-dimensional tensor of fitnesses.

The following example demonstrates how to define and solve the classical Rastrigin problem.



```python

from evotorch import Problem

from evotorch.algorithms import SNES

from evotorch.logging import StdOutLogger, PandasLogger

import math

import matplotlib.pyplot as plt

import torch



# Declare the objective function

def rastrigin(x: torch.Tensor) -> torch.Tensor:

    A = 10

    (_, n) = x.shape

    return A * n + torch.sum((x**2) - A * torch.cos(2 * math.pi * x), 1)



# Declare the problem

problem = Problem(

    "min",

    rastrigin,

    initial_bounds=(-5.12, 5.12),

    solution_length=100,

    vectorized=True,

    # device="cuda:0"  # enable this line if you wish to use GPU

)



# Initialize the SNES algorithm to solve the problem

searcher = SNES(problem, popsize=1000, stdev_init=10.0)



# Initialize a standard output logger, and a pandas logger

_ = StdOutLogger(searcher, interval=10)

pandas_logger = PandasLogger(searcher)



# Run SNES for the specified amount of generations

searcher.run(2000)



# Get the progress of the evolution into a DataFrame with the

# help of the PandasLogger, and then plot the progress.

pandas_frame = pandas_logger.to_dataframe()

pandas_frame["best_eval"].plot()

plt.show()

```



## A reinforcement learning example



The following example demonstrates how to solve reinforcement learning tasks that are available through the gym library.



```python

from evotorch.algorithms import PGPE

from evotorch.logging import StdOutLogger, PicklingLogger

from evotorch.neuroevolution import GymNE



# Declare the problem to solve

problem = GymNE(

    env="Humanoid-v4",  # Solve the Humanoid-v4 task

    network="Linear(obs_length, act_length)",  # Linear policy

    observation_normalization=True,  # Normalize the policy inputs

    decrease_rewards_by=5.0,  # Decrease each reward by 5.0

    num_actors="max",  # Use all available CPUs

    # num_actors=4,    # Explicit setting. Use 4 actors.

)



# Instantiate a PGPE algorithm to solve the problem

searcher = PGPE(

    problem,

    # Base population size

    popsize=200,

    # For each generation, sample more solutions until the

    # number of simulator interactions reaches this threshold

    num_interactions=int(200 * 1000 * 0.75),

    # Stop re-sampling solutions if the current population size

    # reaches or exceeds this number.

    popsize_max=3200,

    # Learning rates

    center_learning_rate=0.0075,

    stdev_learning_rate=0.1,

    # Radius of the initial search distribution

    radius_init=0.27,

    # Use the ClipUp optimizer with the specified maximum speed

    optimizer="clipup",

    optimizer_config={"max_speed": 0.15},

)



# Instantiate a standard output logger

_ = StdOutLogger(searcher)



# Optional: Instantiate a logger to pickle and save the results periodically.

# In this example, among the saved results will be the center of the search

# distribution, since we are using PGPE which is distribution-based.

_ = PicklingLogger(searcher, interval=10)



# Run the algorithm for the specified amount of generations

searcher.run(500)



# Get the center point of the search distribution,

# obtain a policy out of that point, and visualize the

# agent using that policy.

center_solution = searcher.status["center"]

trained_policy = problem.make_net(center_solution)

problem.visualize(trained_policy)

```



More examples can be found [here](examples/).



# How to cite



If you use EvoTorch in your research, please consider citing our [paper](https://arxiv.org/abs/2302.12600).



```bibtex

@article{evotorch2023arxiv,

  title={{EvoTorch}: Scalable Evolutionary Computation in {Python}},

  author={Toklu, Nihat Engin and Atkinson, Timothy and Micka, Vojt\v{e}ch and Liskowski, Pawe\l{} and Srivastava, Rupesh Kumar},

  journal={arXiv preprint},

  year={2023},

  note={https://arxiv.org/abs/2302.12600}

}

```



# How to Contribute



Please see our [contribution guidelines](CONTRIBUTING.md).



# Authors



- [Nihat Engin Toklu](https://github.com/engintoklu)

- [Timothy Atkinson](https://github.com/NaturalGradient)

- [Vojtech Micka](https://github.com/Higgcz)

- [Pawel Liskowski](https://github.com/pliskowski)

- [Rupesh Kumar Srivastava](https://github.com/flukeskywalker)