  This work provides a thorough study on how reward scaling can affect
performance of deep reinforcement learning agents. In particular, we would like
to answer the question that how does reward scaling affect non-saturating ReLU
networks in RL? This question matters because ReLU is one of the most effective
activation functions for deep learning models. We also propose an Adaptive
Network Scaling framework to find a suitable scale of the rewards during
learning for better performance. We conducted empirical studies to justify the
solution.
