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Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa

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RL_Q-Learning_E3

I run the experiment *RL_Q-Learning_E3*, but it doesn't get a good result?It seems that the policy does'nt converge?

Mealoore avatar Mealoore

new_env.py

To changes the position of many obtacles

RenisaGunarAPM19022001 avatar RenisaGunarAPM19022001

FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. self.q_table = self.q_table.append(

DataFrame.Append deprecated and need to be replaced by pandas.concat. DataFrame.Append: def check_state_exist(self, state): if state not in self.q_table.index: self.q_table = self.q_table.append( pd.Series( [0]*len(self.actions), index=self.q_table.columns, name=state, ) ) Pandas.Concat: def check_state_exist(self,...

DatTrongNg avatar DatTrongNg

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Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa

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