  Recent reinforcement learning algorithms, though achieving impressive results
in various fields, suffer from brittle training effects such as regression in
results and high sensitivity to initialization and parameters. We claim that
some of the brittleness stems from variance differences, i.e. when different
environment areas - states and/or actions - have different rewards variance.
This causes two problems: First, the "Boring Areas Trap" in algorithms such as
Q-learning, where moving between areas depends on the current area variance,
and getting out of a boring area is hard due to its low variance. Second, the
"Manipulative Consultant" problem, when value-estimation functions used in DQN
and Actor-Critic algorithms influence the agent to prefer boring areas,
regardless of the mean rewards return, as they maximize estimation precision
rather than rewards. This sheds a new light on how exploration contribute to
training, as it helps with both challenges. Cognitive experiments in humans
showed that noised reward signals may paradoxically improve performance. We
explain this using the two mentioned problems, claiming that both humans and
algorithms may share similar challenges. Inspired by this result, we propose
the Adaptive Symmetric Reward Noising (ASRN), by which we mean adding Gaussian
noise to rewards according to their states' estimated variance, thus avoiding
the two problems while not affecting the environment's mean rewards behavior.
We conduct our experiments in a Multi Armed Bandit problem with variance
differences. We demonstrate that a Q-learning algorithm shows the brittleness
effect in this problem, and that the ASRN scheme can dramatically improve the
results. We show that ASRN helps a DQN algorithm training process reach better
results in an end to end autonomous driving task using the AirSim driving
simulator.
