Description
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.
Papers Using This Method
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC2024-11-06Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks2022-09-16DNA: Proximal Policy Optimization with a Dual Network Architecture2022-06-20Deep Reinforcement Learning at the Edge of the Statistical Precipice2021-08-30Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates2021-01-01A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward2020-09-24SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning2020-07-09Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning2019-04-30Rainbow: Combining Improvements in Deep Reinforcement Learning2017-10-06