To Combine or Not To Combine? A Rainbow Deep Reinforcement Learning Agent for Dialog Policies
In this paper, we explore state-of-the-art deep reinforcement learning methods for dialog policy training such as prioritized experience replay, double deep Q-Networks, dueling network architectures and distributional learning. 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... (read more)
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Double Q-learning
Dense Connections
Convolution
Dueling Network