Multimodal Dual Population Evolutionary Reinforcement Learning

The integration of Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) offers a new paradigm for complex decision-making tasks, especially through methods that optimize actor populations and critics collaboratively (e.g., DBCEM-TD3, QD-RL), collectively referred to as the Actor-Population Critic (APC) framework. However, existing methods still face two major challenges: traditional feature inputs fail to fully capture the spatial relationships, and the design of the critic struggles to balance both the quality and diversity of the policies. To address these issues, we propose Multimodal Dual Population Evolutionary Reinforcement Learning (M-DPERL), which achieves breakthroughs through cross-modal feature augmentation and dual-population coevolution. On one hand, a feature-image bimodal input enhancement mechanism is proposed, which dynamically encodes environmental features into spatial heatmaps. On the other hand, this method introduces a critic population into APC and, for the first time, proposes a population-guided fitness metric to optimize the critic's ability to guide the actor population in balancing quality and diversity. Additionally, we design the Flow-Fix Dynamics (FFD) mechanism to regulate the update rhythm of the dual populations and alleviate the evolutionary chaos in their coevolution. The results across a series of MUJOCO tasks demonstrate that M-DPERL significantly outperforms the baselines, with a 19.2% improvement in sample efficiency and a 17.1% increase in final performance.