# Reward does not increase for a maze escaping problem with DQN

I am using deep reinforcement learning to solve a classic maze escaping task, similar to the implementation provided here, except the following three key differences:

1. instead of using a numpy array as the input of a standard maze escaping task, I am feeding the model with an image at each step; the image is a 1300 * 900 RGB image, so it is not too small.

2. reward:

• each valid move has a small negative reward (penalize long move)
• each invalid move has a big negative reward (run into other objects or boundaries)
• Each blocked move has the minimal reward (not common)
• Find the remote detectors’ defect has a positive reward (5)
3. I tweaked the parameters of replay memory, reduced the size of the replay memory buffer.

Regarding the implementation, I basically do not change the agent setup except the above items, and I implemented my env to wrap my customized maze.

But the problem is that, the accumulated reward (first 200 rounds of successful escaping) does not increase:

And the number of steps it takes to escape one maze is also stable somewhat:

Here are my question, on which aspect I could start to look at to optimize my problem? Or is it still too early and I will need to train more time?