EMI-Group/evorl

EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.

284 40 +0/wk

GitHub

automl evolutionary-algorithms gpu-acceleration hpo jax policy-gradient population-based-training ppo reinforcement-learning reinforcement-learning-agent reinforcement-learning-environments sac

Trend 3

Star & Fork Trend (15 data points)

Stars

Forks

Multi-Source Signals

GitHub

stars 284

forks 40

Growth Velocity

EMI-Group/evorl has +0 stars this period . 7-day velocity: 0.3%.

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Signal-backed technical analysis will be available soon.

Metric	evorl	colette	Machine-learning-interview	radient
Stars	284	285	285	283
Forks	40	30	21	11
Weekly Growth	+0	+0	+1	+0
Language	Python	HTML	N/A	Python
Sources	1	1	1	1
License	GPL-3.0	NOASSERTION	N/A	BSD-2-Clause

Capability Radar vs colette

evorl

colette

Maintenance Activity 100

Last code push 3 days ago.

Community Engagement 70

Fork-to-star ratio: 14.1%. Active community forking and contributing.

Issue Burden 70

Issue data not yet available.

Growth Momentum 30

No measurable growth in the current period (first-day cold start expected).

License Clarity 70

Licensed under GPL-3.0. Copyleft — check compatibility requirements.

Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.