  In reinforcement learning, it is often difficult to automate
high-dimensional, rapid decision-making in dynamic environments, especially
when domains require real-time online interaction and adaptive strategies such
as web-based games. This work proposes a state-of-the-art Flexible Deep
Q-Network (FDQN) framework that can address this challenge with a selfadaptive
approach that is processing high-dimensional sensory data in realtime using a
CNN and dynamically adapting the model architecture to varying action spaces of
different gaming environments and outperforming previous baseline models in
various Atari games and the Chrome Dino game as baselines. Using the
epsilon-greedy policy, it effectively balances the new learning and
exploitation for improved performance, and it has been designed with a modular
structure that it can be easily adapted to other HTML-based games without
touching the core part of the framework. It is demonstrated that the FDQN
framework can successfully solve a well-defined task in a laboratory condition,
but more importantly it also discusses potential applications to more
challenging real-world cases and serve as the starting point for future further
exploration into automated game play and beyond.
