uber-research/deep-neuroevolution

Health v2 · maintenance only

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Adoption not part of the score

1667 stars · 297 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Distributed implementations of deep neuroevolution algorithms (ES, NS-ES, NSR-ES, DeepGA, Random Search) from Uber AI Labs research papers, for training deep neural networks on reinforcement learning tasks. It includes a VINE visualization tool for inspecting neuroevolution experiments and a GPU-accelerated implementation.

Use cases

• train deep neural networks for reinforcement learning with evolution strategies
• run genetic algorithms on Atari games
• compare neuroevolution against gradient-based RL methods
• visualize neuroevolution experiment populations with VINE
• run distributed RL experiments on AWS with Redis
• reproduce Uber AI deep neuroevolution paper results

When to choose

• you want to experiment with evolution strategies or genetic algorithms for deep RL
• you need to reproduce or extend the Deep Neuroevolution papers
• you want population-based RL training that avoids gradient backpropagation
• you need an interactive visualization of neuroevolution search

When to avoid

• you need standard gradient-based RL like PPO or DQN
• you need a maintained production library with active support
• you cannot obtain a MuJoCo license for humanoid experiments
• you need Windows support or a simple pip-installable package

Facets

library · maturity maintenance

reinforcement-learning machine-learning data-visualization gpu-computing reinforcement-learning machine-learning artificial-intelligence python cloud neuroevolution evolution-strategies genetic-algorithms atari mujoco research-code distributed-computing redis research linux macos docker

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markdown · JSON · MCP: product_card(name="uber-research/deep-neuroevolution")