RL - Weighthing negative rewards Let's consider that I give an agent a reward of -1 (minimum reward) every time it performs an action which leads to the premature end of the episode (i.e., the agent dies). Besides, I also give a negative reward in the [-1, 0) interval when the agent performs an action that I want to avoid to repeat too much. However, these actions do not terminate an episode.

Is it possible that the agent will learn to take the "-1" action given that it is possible to collect many negative rewards which in the end will sum less than -1? This is, the agent will prefer to commit suicide rather than experience many negative rewards.

PD: the agent also receives positive rewards in the (0, 1] interval, which tend to be of higher magnitude than the negative rewards but the agent receives them less often when it is exploring.
Yes, it is possible. In my case, with a specific reward function, my agent (snake) preferred to suicide instead of trying to reach the target, because of the "live penalty" (the agent receives a penalty each step, to speed up the exploitation phase over the exploration). Check this blog posts, they go through all of this in detail and they're so far the best ones I have found:
https://mpatacchiola.github.io/blog/2017/01/29/dissecting-reinforcement-learning-3.html

Also, since the author has a background in Cognitive Neuroscience, each algorithm is explained from a neuroscience point of view.