{"url":"/method/rainbow-dqn","slug":"rainbow-dqn","name":"Rainbow DQN","full_name":"Rainbow DQN","full_name_withheld":false,"description_markdown":"**Rainbow DQN** is an extended [DQN](https://paperswithcode.com/method/dqn) that combines several improvements into a single learner. Specifically:\r\n\r\n- It uses [Double Q-Learning](https://paperswithcode.com/method/double-q-learning) to tackle overestimation bias.\r\n- It uses [Prioritized Experience Replay](https://paperswithcode.com/method/prioritized-experience-replay) to prioritize important transitions.\r\n- It uses [dueling networks](https://paperswithcode.com/method/dueling-network).\r\n- It uses [multi-step learning](https://paperswithcode.com/method/n-step-returns).\r\n- It uses distributional reinforcement learning instead of the expected return.\r\n- It uses noisy linear layers for exploration.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Rainbow: Combining Improvements in Deep Reinforcement Learning","paper":"/paper/rainbow-combining-improvements-in-deep","first_author":"Matteo Hessel","n_authors":10,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/rainbow-combining-improvements-in-deep"},"source":{"url":"http://arxiv.org/abs/1710.02298v1","title":"Rainbow: Combining Improvements in Deep Reinforcement Learning","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Reinforcement Learning","area_id":"reinforcement-learning","collection":"Q-Learning Networks","url":"/methods/category/q-learning-networks","pwc_aliases":["q-learning"]}],"n_papers_tagged":9,"archive_num_papers":9,"papers_newest_first":[{"paper":"/paper/beyond-the-rainbow-high-performance-deep","title":"Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC","date":"2024-11-06","arxiv_id":"2411.03820","n_code_links":3,"syntology":{"ran":13,"of":25,"unverified":12,"pointer_only":21}},{"paper":"/paper/reducing-variance-in-temporal-difference","title":"Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks","date":"2022-09-16","arxiv_id":"2209.07670","n_code_links":1,"syntology":null},{"paper":"/paper/dna-proximal-policy-optimization-with-a-dual","title":"DNA: Proximal Policy Optimization with a Dual Network Architecture","date":"2022-06-20","arxiv_id":"2206.10027","n_code_links":1,"syntology":null},{"paper":"/paper/deep-reinforcement-learning-at-the-edge-of","title":"Deep Reinforcement Learning at the Edge of the Statistical Precipice","date":"2021-08-30","arxiv_id":"2108.13264","n_code_links":3,"syntology":{"ran":5,"of":5,"unverified":0,"pointer_only":0}},{"paper":null,"title":"Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"title":"A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward","date":"2020-09-24","arxiv_id":"2009.11905","n_code_links":0,"syntology":null},{"paper":"/paper/sunrise-a-simple-unified-framework-for","title":"SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning","date":"2020-07-09","arxiv_id":"2007.04938","n_code_links":1,"syntology":null},{"paper":null,"title":"Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning","date":"2019-04-30","arxiv_id":"1904.13255","n_code_links":0,"syntology":null},{"paper":"/paper/rainbow-combining-improvements-in-deep","title":"Rainbow: Combining Improvements in Deep Reinforcement Learning","date":"2017-10-06","arxiv_id":"1710.02298","n_code_links":34,"syntology":{"ran":2,"of":6,"unverified":4,"pointer_only":1}}],"papers_shown":9,"tasks":[{"task":"/task/reinforcement-learning-1","name":"Reinforcement Learning (RL)","papers":7},{"task":"/task/deep-reinforcement-learning","name":"Deep Reinforcement Learning","papers":6},{"task":"/task/reinforcement-learning-2","name":"reinforcement-learning","papers":4},{"task":"/task/q-learning","name":"Q-Learning","papers":3},{"task":"/task/reinforcement-learning","name":"Reinforcement Learning","papers":3},{"task":"/task/atari-games","name":"Atari Games","papers":2},{"task":"/task/computational-efficiency","name":"Computational Efficiency","papers":1},{"task":"/task/decision-making","name":"Decision Making","papers":1},{"task":"/task/diversity","name":"Diversity","papers":1},{"task":"/task/efficient-exploration","name":"Efficient Exploration","papers":1},{"task":"/task/ensemble-learning","name":"Ensemble Learning","papers":1},{"task":"/task/game-of-go","name":"Game of Go","papers":1},{"task":"/task/montezumas-revenge","name":"Montezuma's Revenge","papers":1}],"tasks_shown":13,"n_tasks":13,"usage_by_year":[{"year":"2017","papers":1},{"year":"2019","papers":1},{"year":"2020","papers":2},{"year":"2021","papers":2},{"year":"2022","papers":2},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/rainbow-dqn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}