About the reinforcement-learning category
A section to discuss RL implementations, research, problems
smth
lakehanne
I had the same problem, it only reached at 199 at my env, then back and force…
elynn
I got it work the first few times I ran it, but later without any changes the same situation happened as you mentioned. Very weird. Thought it’s using the same random seed thought out.
howard50b
Dear all, One interesting thing that I am observing is that, after a few training iterations (one match is enough), my Q-network starts outputting zeros regardless of the input state! Initially, I thought there was a bug in my code, but now I think that it somehow makes sense. In Pong, the obtained reward is almost always zero (except in the frames where we score or concede a goal) and the Bellman equation is: Q(s,a) = reward + GAMMA * max_a’ (Q(s’,a’)) so, every time we get a zero reward, the Bellman equation is easily satisfied if Q(s,a) = max_a’ (Q(s’,a’)) = 0. That’s why I think my Q-network is basically learning to output zeros regardless of the input… Any hints on how I can overcome this issue? I am following the exact same methodology as in DeepMind’s paper, including the network architecture and the preprocessing.
dpernes
mathematics
Hi everyone, If any of you could help to clarify this uncertainty, it would be much appreciated. Thank you.
cruzas