I’m trying to wrap my head around REINFORCE and in particular this line 15. This would appear to result in a loss of 0 if log_prob is 0 or if the reward (return) is 0. In the latter case, a very high return with 100% probability would look just like no return with any probability. I’m not sure how it could learn under such confounded signals. What am I missing?