In this work, we analyze and improve upon reinforcement learning techniques used to build agents that can learn to play Infinite Mario, an action game. We use the object-oriented representation with the hierarchical RL model as a learning framework. We then extend the idea of hierarchical RL by designing a hierarchy in action selection using domain specific knowledge. Using experimental results, we show that this approach facilitates faster and efficient learning for the domain.
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