Improving DQN with fluctuations 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?
It is not clear form your question, how you use your replay buffer. Basically, you have to store all states/reward tuples and train your agent on the entire buffer.
Moreover, you should give the agent time to explore (all) states of the world. But if you want to speed up training, you can try to implement importance sampling