Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task

• URL: http://arxiv.org/abs/2108.08911v1
• Date: Wed, 18 Aug 2021 02:49:49 GMT
• Title: Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task
• Authors: Angel Ayala, Francisco Cruz, Bruno Fernandes and Richard Dazeley
• Abstract summary: introspection-based method that transforms Q-values into probabilities of success used as base to explain agent's decision-making process. We adapt the introspection method to be used in a non-episodic task and try it in a continuous Atari game scenario solved with the Rainbow algorithm.
• Score: 1.2735892003153293
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Explainable reinforcement learning allows artificial agents to explain their behavior in a human-like manner aiming at non-expert end-users. An efficient alternative of creating explanations is to use an introspection-based method that transforms Q-values into probabilities of success used as the base to explain the agent's decision-making process. This approach has been effectively used in episodic and discrete scenarios, however, to compute the probability of success in non-episodic and more complex environments has not been addressed yet. In this work, we adapt the introspection method to be used in a non-episodic task and try it in a continuous Atari game scenario solved with the Rainbow algorithm. Our initial results show that the probability of success can be computed directly from the Q-values for all possible actions.

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