Hello :) I'm pretty new to this community, so let me know if I posted anything incorrectly and I'll try to change it.
I'm working on the project which aim is to create self-driving agent in CARLA. I built a neural network Xception (decaying ε-greedy). The other parameters are:
EPISODES: 100
GAMMA: 0.3
EPSILON_DECAY: 0.9
MIN_EPSILON: 0.001 BATCH: 16
Due to the limited computer resources I chose 100 or 300 epochs to train the model, but it generates much fluctuations:
EPISODES: 100
GAMMA: 0.7 EPSILON_DECAY: 0.9
MIN_EPSILON: 0.001 BATCH: 16
Can anyone suggest how can I improve my results? Or it is only the issue of small number of epochs?