## Appendix A  
```
Input: Initial state \(s\), Action set \(a\), update sound \(k\), learning rate \(\propto\), discount factor \(\beta\) Notations:\(t\)--time interval between sounds, \(y_{t}\)--action in time for the specific sounds, \(s_{t}\)--state of time for the dog sound, \(r_{t}\)--reward for the state, \(A\)--Sample set for \(Q\) updating, \(\lambda\)--to determine update functioning of \(Q\) initiate \(i=0\), \(t=0\), \(Q_{0}(s_{0},a_{0})=0\) for time interval \(t\) between sounds do \(y_{t}\)\(\leftarrow\) action selection \((y,Q_{t}(s_{t},y_{t}))\) \(r_{t}\)\(\leftarrow\) get reward \((s_{t},y_{t})\) \(Q_{t+1}(s_{t},y_{t})\)\(\leftarrow\)\(Q_{t}(s_{t},y_{t})+\propto_{t}[r_{t+1}+\beta\times max_{a\in A}Q_{t}(s_{t+1},\ y)-Q_{t}(s_{t},\ y_{t})]\) // Updating function \(A\)\(\leftarrow\)\(A\cup(s_{t},y_{t},\



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