Zero Shot Cancer

RL environment for autonomous biologist agents. Simulate an entire biological worldstate based on scientifically-accurate single-cell standards. Naive agents try to solve a frontier problem, such as identifying key pathways and markers for cancer growth, and have access to 40+ tool calls for fully implemented bioinformatics procedures. Agents probe and modify the world state and try to recover the generated "truth" of the given world using intermediate experimental results to guide future thinking. Problems range in difficulty and complexity, requiring more and more complex thought and workflows. Rewards are based on feasible flow of experiments and the accuracy of the final study conclusion, based on how close the recovered world state is to the hidden "true" world state.