evosax: JAX-Based Evolution Strategies 🦎

Tired of having to handle asynchronous processes for neuroevolution? Do you want to leverage massive vectorization and high-throughput accelerators for evolution strategies (ES)? evosax allows you to leverage JAX, XLA compilation and auto-vectorization/parallelization to scale ES to your favorite accelerators. The API is based on the classical ask, evaluate, tell cycle of ES. Both ask and tell calls are compatible with jit, vmap/pmap and lax.scan. It includes a vast set of both classic (e.g. CMA-ES, Differential Evolution, etc.) and modern neuroevolution (e.g. OpenAI-ES, Augmented RS, etc.) strategies. You can get started here 👉

Basic evosax API Usage 🍲

import jax
from evosax import CMA_ES

# Instantiate the search strategy
rng = jax.random.PRNGKey(0)
strategy = CMA_ES(popsize=20, num_dims=2, elite_ratio=0.5)
es_params = strategy.default_params
state = strategy.initialize(rng, es_params)

# Run ask-eval-tell loop - NOTE: By default minimization!
for t in range(num_generations):
    rng, rng_gen, rng_eval = jax.random.split(rng, 3)
    x, state = strategy.ask(rng_gen, state, es_params)
    fitness = ...  # Your population evaluation fct 
    state = strategy.tell(x, fitness, state, es_params)

# Get best overall population member & its fitness
state["best_member"], state["best_fitness"]

Implemented Evolution Strategies 🦎

Strategy	Reference	Import	Example
OpenES	Salimans et al. (2017)	OpenES
PGPE	Sehnke et al. (2010)	PGPE
ARS	Mania et al. (2018)	ARS
CMA-ES	Hansen & Ostermeier (2001)	CMA_ES
Simple Gaussian	Rechenberg (1978)	SimpleES
Simple Genetic	Such et al. (2017)	SimpleGA
x-NES	Wierstra et al. (2014)	xNES
Particle Swarm Optimization	Kennedy & Eberhart (1995)	PSO
Differential Evolution	Storn & Price (1997)	DE
Persistent ES	Vicol et al. (2021)	PersistentES
Population-Based Training	Jaderberg et al. (2017)	PBT
Sep-CMA-ES	Ros & Hansen (2008)	Sep_CMA_ES
BIPOP-CMA-ES	Hansen (2009)	BIPOP_CMA_ES
IPOP-CMA-ES	Auer & Hansen (2005)	IPOP_CMA_ES
Full-iAMaLGaM	Bosman et al. (2013)	Full_iAMaLGaM
Independent-iAMaLGaM	Bosman et al. (2013)	Indep_iAMaLGaM
MA-ES	Bayer & Sendhoff (2017)	MA_ES
LM-MA-ES	Loshchilov et al. (2017)	LM_MA_ES
RmES	Li & Zhang (2017)	RmES	TBC
GLD	Golovin et al. (2019)	GLD	TBC

Installation ⏳

The latest evosax release can directly be installed from PyPI:

pip install evosax

If you want to get the most recent commit, please install directly from the repository:

pip install git+https://github.com/RobertTLange/evosax.git@main

In order to use JAX on your accelerators, you can find more details in the JAX documentation.

Examples 📖

• 📓 Classic ES Tasks: API introduction on Rosenbrock function (CMA-ES, Simple GA, etc.).
• 📓 CartPole-Control: OpenES & PEPG on the CartPole-v1 gym task (MLP/LSTM controller).
• 📓 MNIST-Classifier: OpenES on MNIST with CNN network.
• 📓 LRateTune-PES: Persistent ES on meta-learning problem as in Vicol et al. (2021).
• 📓 Quadratic-PBT: PBT on toy quadratic problem as in Jaderberg et al. (2017).
• 📓 Restart-Wrappers: Custom restart wrappers as e.g. used in (B)IPOP-CMA-ES.

Key Selling Points 💵

• Strategy Diversity: evosax implements more than 10 classical and modern neuroevolution strategies. All of them follow the same simple ask/eval API and come with tailored tools such as the ClipUp optimizer, parameter reshaping into PyTrees and fitness shaping (see below).

• Vectorization/Parallelization of ask/tell Calls: Both ask and tell calls can leverage jit, vmap/pmap. This enables vectorized/parallel rollouts of different evolution strategies.

from evosax import ARS
# E.g. vectorize over different lrate decays
strategy = ARS(popsize=100, num_dims=20)
es_params = {
    "lrate_decay": jnp.array([0.999, 0.99, 0.9]),
    ...
}
map_dict = {
    "lrate_decay": 0,
    ...
}

# Vmap-composed batch initialize, ask and tell functions 
batch_init = jax.vmap(strategy.init, in_axes=(None, map_dict))
batch_ask = jax.vmap(strategy.ask, in_axes=(None, 0, map_dict))
batch_tell = jax.vmap(strategy.tell, in_axes=(0, 0, 0, map_dict))

• Scan Through Evolution Rollouts: You can also lax.scan through entire init, ask, eval, tell loops for fast compilation of ES loops:

@partial(jax.jit, static_argnums=(1,))
def run_es_loop(rng, num_steps):
    """Run evolution ask-eval-tell loop."""
    es_params = strategy.default_params
    state = strategy.initialize(rng, es_params)

    def es_step(state_input, tmp):
        """Helper es step to lax.scan through."""
        rng, state = state_input
        rng, rng_iter = jax.random.split(rng)
        x, state = strategy.ask(rng_iter, state, es_params)
        fitness = ...
        state = strategy.tell(y, fitness, state, es_params)
        return [rng, state], fitness[jnp.argmin(fitness)]

    _, scan_out = jax.lax.scan(es_step,
                               [rng, state],
                               [jnp.zeros(num_steps)])
    return jnp.min(scan_out)

• Population Parameter Reshaping: We provide a ParamaterReshaper wrapper to reshape flat parameter vectors into PyTrees. The wrapper is compatible with JAX neural network libraries such as Flax/Haiku and makes it easier to afterwards evaluate network populations.

from flax import linen as nn
from evosax import ParameterReshaper

class MLP(nn.Module):
    num_hidden_units: int
    ...

    @nn.compact
    def __call__(self, obs):
        ...
        return ...

network = MLP(64)
policy_params = network.init(rng, jnp.zeros(4,), rng)

# Initialize reshaper based on placeholder network shapes
param_reshaper = ParameterReshaper(policy_params["params"])

# Get population candidates & reshape into stacked pytrees
x = strategy.ask(...)
x_shaped = param_reshaper.reshape(x)

• Flexible Fitness Shaping: By default evosax assumes that the fitness objective is to be minimized. If you would like to maximize instead, perform rank centering, z-scoring or add weight regularization you can use the FitnessShaper:

from evosax import FitnessShaper

# Instantiate jittable fitness shaper (e.g. for Open ES)
fit_shaper = FitnessShaper(centered_rank=True,
                           z_score=True,
                           weight_decay=0.01,
                           maximize=True)

# Shape the evaluated fitness scores
fit_shaped = fit_shaper.apply(x, fitness) 

• Strategy Restart Wrappers: You can also choose from a set of different restart mechanisms, which will relaunch a strategy (with e.g. new population size) based on termination criteria. Note: For all restart strategies which alter the population size the ask and tell methods will have to be re-compiled at the time of change.

from evosax import CMA_ES
from evosax.restarts import BIPOP_Restarter

# Define a termination criterion (kwargs - fitness, state, params)
def std_criterion(fitness, state, params):
    """Restart strategy if fitness std across population is small."""
    return fitness.std() < 0.001

# Instantiate Base CMA-ES & wrap with BIPOP restarts
# Pass strategy-specific kwargs separately (e.g. elite_ration or opt_name)
strategy = CMA(num_dims, popsize, elite_ratio)
re_strategy = BIPOP_Restarter(
                strategy,
                stop_criteria=[std_criterion],
                strategy_kwargs={"elite_ratio": elite_ratio}
            )
state = re_strategy.initialize(rng, es_params)

# ask/tell loop - restarts are automatically handled 
rng, rng_gen, rng_eval = jax.random.split(rng, 3)
x, state = re_strategy.ask(rng_gen, state, params)
fitness = ...  # Your population evaluation fct 
state = re_strategy.tell(x, fitness, state, params)

Resources & Other Great JAX-ES Tools 📝

• 📺 Rob's MLC Research Jam Talk: Small motivation talk at the ML Collective Research Jam.
• 📝 Rob's 02/2021 Blog: Tutorial on CMA-ES & leveraging JAX's primitives.
• 💻 Evojax: JAX-ES library by Google Brain with great rollout wrappers.
• 💻 QDax: Quality-Diversity algorithms in JAX.

Citing evosax ✏️

If you use evosax in your research, please cite it as follows:

@software{evosax2022github,
  author = {Robert Tjarko Lange},
  title = {{evosax}: JAX-based Evolution Strategies},
  url = {http://github.com/RobertTLange/evosax},
  year = {2022},
}

Development 👷

You can run the test suite via python -m pytest -vv --all. If you find a bug or are missing your favourite feature, feel free to create an issue and/or start contributing 🤗.