# Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics

arXiv: https://arxiv.org/abs/2605.15769 · HTML: https://mlsanity.com/paper/2605.15769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 15 May 2026.

## Builds on (most cited first, 12 of 12)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots](https://mlsanity.com/paper/2201.09863/) (2022)
- [Scalable Co-Optimization of Morphology and Control in Embodied Machines](https://mlsanity.com/paper/1706.06133/) (2017)
- [Learning Directed Locomotion in Modular Robots with Evolvable Morphologies](https://mlsanity.com/paper/2001.07804/) (2020)
- [The Effects of Learning in Morphologically Evolving Robot Systems](https://mlsanity.com/paper/2111.09851/) (2021)
- [Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots](https://mlsanity.com/paper/2402.09231/) (2024)
- [Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots](https://mlsanity.com/paper/2306.09358/) (2023)
- [Evolutionary Brain-Body Co-Optimization Consistently Fails to Select for Morphological Potential](https://mlsanity.com/paper/2508.17464/) (2025)
- [Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments](https://mlsanity.com/paper/2403.19545/) (2024)
- [Generational Replacement and Learning for High-Performing and Diverse Populations in Evolvable Robots](https://mlsanity.com/paper/2601.03807/) (2026)
- [Social Learning Strategies for Evolved Virtual Soft Robots](https://mlsanity.com/paper/2604.12482/) (2026)

## Abstract (arXiv)

The co-optimization of a robot's body and brain presents a coupled challenge: the morphology constrains which control strategies are effective, while the control determines how well the morphology performs. To address this, we combine morphology optimization as evolution with controller optimization as lifetime learning, utilizing Lamarckian inheritance to transfer learned controller parameters from parent to offspring. In dynamic environments, existing literature presents conflicting evidence: while traditional evolutionary theory often suggests Lamarckian inheritance lacks benefit, recent studies in evolutionary robotics indicate it can improve performance. We hypothesize that this is because previous works have not included all relevant variables with dynamic environments. In this work, we show that the benefit of Lamarckian inheritance depends on two variables: how conflicting the environmental changes are to robot control, and the predictability of those changes for the robotic agent. Using virtual soft robots and two different learning approaches, Bayesian optimization and reinforcement learning, we show that Lamarckian inheritance only underperforms Darwinian inheritance when the changes are both conflicting and unpredictable. We find that adding a sensor to detect environmental changes restores the benefits for Lamarckian inheritance in conflicting environments, by allowing robotic agents to predict the need for a different behavior, thereby generalizing their control.