A DQN trained on random-forest-simulated bladder cancer trajectories reports high internal rewards, but the evaluation is circular and the reported numbers are not supported by the data.
A review on modeling tumor dynamics and agent reward functions in reinforcement learning based therapy optimization
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Enhancing Personalized Bladder Cancer Treatment Through Reinforcement Learning: A Recurrent Patient State Transition Decision Support Framework
A DQN trained on random-forest-simulated bladder cancer trajectories reports high internal rewards, but the evaluation is circular and the reported numbers are not supported by the data.