Papers › A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable...
A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-Goals
Connor van Rossum, Candice Feinberg, Adam Abu Shumays, Kyle Baxter, Benedek Bartha
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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