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Reinforcement-learning-q-learning

  • This is a very simple example to show how q_learning works

  • This is the most important formula of q_learning: Q(state,x1)= oldQ + alpha * (R(state,x1)+ (gamma * MaxQ(x1)) - oldQ)

Through this program you can see the agent how to learn that find the best way to reach it's goal. If agent bump into the wall, we will give -1 as the negative reward. On the controry, if agent hit the goal, we will give +1 as the positive reward. You will see the q table gradually being an optimizing value and converge to the opitimal value.

  • Here is q table. Red dot means agent's position.

q table

agent move

Instruction

  • q_learning.m: The basic version of map
  • q_learning_matrix_n.m: You can modify the map size by yourself and don't forget to modify the max_round as well
goal_x=??;
goal_y=??;
max_round=??;
  • plot_action.m:Show q table