Pytorch implementation of Evolutionary Policy Optimization, from Wang et al. of the Robotics Institute at Carnegie Mellon University
This paper stands out, as I have witnessed the positive effects first hand in an exploratory project (mixing evolution with gradient based methods). Perhaps the Alexnet moment for genetic algorithms has not come to pass yet.
Besides their latent variable strategy, I'll also throw in some attempts with crossover in weight space
$ pip install evolutionary-policy-optimizationimport torch
from evolutionary_policy_optimization import (
LatentGenePool,
Actor,
Critic
)
latent_pool = LatentGenePool(
num_latents = 128,
dim_latent = 32,
)
state = torch.randn(1, 32)
actor = Actor(dim_state = 32, dim = 256, mlp_depth = 2, num_actions = 4, dim_latent = 32)
critic = Critic(dim_state = 32, dim = 256, mlp_depth = 3, dim_latent = 32)
latent = latent_pool(latent_id = 2)
actions = actor(state, latent)
value = critic(state, latent)
# interact with environment and receive rewards, termination etc
# derive a fitness score for each gene / latent
fitness = torch.randn(128)
latent_pool.genetic_algorithm_step(fitness) # update latent genes with genetic algorithmEnd to end learning
import torch
from evolutionary_policy_optimization import (
create_agent,
EPO,
Env
)
agent = create_agent(
dim_state = 512,
num_latents = 16,
dim_latent = 32,
actor_num_actions = 5,
actor_dim = 256,
actor_mlp_depth = 2,
critic_dim = 256,
critic_mlp_depth = 3,
latent_gene_pool_kwargs = dict(
frac_natural_selected = 0.5
)
)
epo = EPO(
agent,
episodes_per_latent = 1,
max_episode_length = 10,
action_sample_temperature = 1.
)
env = Env((512,))
epo(agent, env, num_learning_cycles = 5)
# saving and loading
agent.save('./agent.pt', overwrite = True)
agent.load('./agent.pt')At the project root, run
$ pip install '.[test]' # or `uv pip install '.[test]'`Then add your tests to tests/test_epo.py and run
$ pytest tests/That's it
@inproceedings{Wang2025EvolutionaryPO,
title = {Evolutionary Policy Optimization},
author = {Jianren Wang and Yifan Su and Abhinav Gupta and Deepak Pathak},
year = {2025},
url = {https://api.semanticscholar.org/CorpusID:277313729}
}@article{Farebrother2024StopRT,
title = {Stop Regressing: Training Value Functions via Classification for Scalable Deep RL},
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journal = {ArXiv},
year = {2024},
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booktitle = {Neural Information Processing Systems},
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}Evolution is cleverer than you are. - Leslie Orgel