Rainbow DQN

2000년 도입 · 논문 9편에서 사용

Rainbow DQN is an extended DQN that combines several improvements into a single learner. Specifically: - It uses Double Q-Learning to tackle overestimation bias. - It uses Prioritized Experience Replay to prioritize important transitions. - It uses dueling networks. - It uses multi-step learning. - It uses distributional reinforcement learning instead of the expected return. - It uses noisy linear layers for exploration.

출처: Rainbow: Combining Improvements in Deep Reinforcement Learning

소개 논문: Rainbow: Combining Improvements in Deep Reinforcement Learning