Rainbow DQN
Rainbow DQN
**Rainbow DQN** is an extended [DQN](https://paperswithcode.com/method/dqn) that combines several improvements into a single learner. Specifically:

- It uses [Double Q-Learning](https://paperswithcode.com/method/double-q-learning) to tackle overestimation bias.
- It uses [Prioritized Experience Replay](https://paperswithcode.com/method/prioritized-experience-replay) to prioritize important transitions.
- It uses [dueling networks](https://paperswithcode.com/method/dueling-network).
- It uses [multi-step learning](https://paperswithcode.com/method/n-step-returns).
- It uses distributional reinforcement learning instead of the expected return.
- It uses noisy linear layers for exploration.
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://paperswithcode.com/paper/rainbow-combining-improvements-in-deep
Rainbow: Combining Improvements in Deep Reinforcement Learning