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

Understand

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.