  In recent years, there have been many deep structures for Reinforcement
Learning, mainly for value function estimation and representations. These
methods achieved great success in Atari 2600 domain. In this paper, we propose
an improved architecture based upon Dueling Networks, in this architecture,
there are two separate estimators, one approximate the state value function and
the other, state advantage function. This improvement based on Maximum Entropy,
shows better policy evaluation compared to the original network and other
value-based architectures in Atari domain.
