Simulus: Combining Improvements in Sample-Efficient World Model Agents

ArXi:2502.11537v4 Announce Type: replace-cross World models (WMs) represent the frontier of sample-efficient reinforcement learning, but their complexity leaves many promising improvements unrealized due to the significant expertise and effort required to identify and integrate them. Inspired by Rainbow, which showed that individually known improvements to DQN complement each other and can be effectively combined, we take on this challenge and ask whether the same principle applies to world model agents. We.