4. Maze exploration using reinforcement learning

The following file is this example in Matlab.

Zip file(m file)：DQN_sample_e.zip
How to use: Run sw_Q _ Learning.m from the DQN_sample folder

As an example using reinforcement learning, we deal with the 3 × 3 maze search problem shown in Fig. 3.

Agents: People
State: Where in S1 ~ S9 the agent is

Action: Move in the direction of「→」，「↑」，「←」，「↓」
Rewards:
            S5, S7, S8: Negative reward (demon)
            S9: Positive reward (money)
            Else: No reward

In the case of this maze search problem, the purpose of deep reinforcement learning is to get maximum reward (money) at the time of goal (When we arrived at S 9,).
→ Do not go through a trout with demons.