A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-Goals

• URL: http://arxiv.org/abs/2104.06630v1
• Date: Wed, 14 Apr 2021 05:21:13 GMT
• Title: A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-Goals
• Authors: Connor van Rossum, Candice Feinberg, Adam Abu Shumays, Kyle Baxter, Benedek Bartha
• Abstract summary: Two key challenges within Reinforcement Learning involve improving (a) agent learning within environments with sparse extrinsic rewards and (b) the explainability of agent actions. We describe a curious subgoal focused agent to address both these challenges. We use a novel method for curiosity produced from a Generative Adrial Network (GAN) based model of environment transitions that is robust to environment transitions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Two key challenges within Reinforcement Learning involve improving (a) agent learning within environments with sparse extrinsic rewards and (b) the explainability of agent actions. We describe a curious subgoal focused agent to address both these challenges. We use a novel method for curiosity produced from a Generative Adversarial Network (GAN) based model of environment transitions that is robust to stochastic environment transitions. Additionally, we use a subgoal generating network to guide navigation. The explainability of the agent's behavior is increased by decomposing complex tasks into a sequence of interpretable subgoals that do not require any manual design. We show that this method also enables the agent to solve challenging procedurally-generated tasks that contain stochastic transitions above other state-of-the-art methods.

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