Reinforcement learning for human-machine collaborative optimization: Application in ground water monitoring

In this paper, we introduce reinforcement learning as a methodology to solve complex multi-criteria optimization problems for ground water monitoring. Multiple analytical criteria are used to assess design decisions and human feedback is simulated by adding random noise. Different learning automata based reinforcement learning methods as well as a genetic algorithm based method are used in experimental studies, which demonstrate the efficiency of reinforcement learning approaches.