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:
instead of using a
numpyarray 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.
- 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)
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?