To Combine or Not To Combine? A Rainbow Deep Reinforcement Learning Agent for Dialog Policies
Dirk Väth, Ngoc Thang Vu · 2019
We explore state-of-the-art deep reinforcement learning methods such as prioritized experience replay, double deep Q-Networks, dueling network architectures, distributional learning methods for dialog policy.Our main findings show that each individual method improves the rewards and the task success rate but combining these methods in a Rainbow agent, which performs best across tasks and environments, is a non-trivial task.We, therefore, provide insights about the influence of each method on the combination and how to combine them to form the Rainbow agent.