Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples

Understand

Despite the fact that deep reinforcement learning (RL) has surpassed human-level performances in various tasks, it still has several fundamental challenges.

• First, most RL methods require intensive data from the exploration of the environment to achieve satisfactory performance.
• Second, the use of neural networks in RL renders it hard to interpret the internals of the system in a way that humans can understand.
• To address these two challenges, we propose a framework that enables an RL agent to reason over its exploration process and distill high-level knowledge for effectively guiding its future explorations.