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Learn to Interpret Atari Agents
Deep Reinforcement Learning (DeepRL) agents surpass human-level performances
in a multitude of tasks. However, the direct mapping from states to actions
makes it hard to interpret the rationale behind the decision making of agents.
In contrast to previous a-posteriori methods of visualizing DeepRL policies, we
propose an end-to-end trainable framework based on Rainbow, a representative
Deep Q-Network (DQN) agent. Our method automatically learns important regions
in the input domain, which enables characterizations of the decision making and
interpretations for non-intuitive behaviors. Hence we name it Region Sensitive
Rainbow (RS-Rainbow). RS-Rainbow utilizes a simple yet effective mechanism to
incorporate visualization ability into the learning model, not only improving
model interpretability, but leading to improved performance. Extensive
experiments on the challenging platform of Atari 2600 demonstrate the
superiority of RS-Rainbow. In particular, our agent achieves state of the art
at just 25% of the training frames. Demonstrations and code are available at
https://github.com/yz93/Learn-to-Interpret-Atari-Agents
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