AI RESEARCH
Evolution Strategies for Deep RL pretraining
arXiv CS.LG
•
ArXi:2604.00066v1 Announce Type: new Although Deep Reinforcement Learning has proven highly effective for complex decision-making problems, it demands significant computational resources and careful parameter adjustment in order to develop successful strategies. Evolution strategies offer a straightforward, derivative-free approach that is less computationally costly and simpler to deploy. However, ES generally do not match the performance levels achieved by DRL, which calls into question their suitability for demanding scenarios.