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The two streams are combined via a special aggregating layer to produce an\r\nestimate of the state-action value function Q as shown in the figure to the right.\r\n\r\nThe last module uses the following mapping:\r\n\r\n$$ Q\\left(s, a, \\theta, \\alpha, \\beta\\right) =V\\left(s, \\theta, \\beta\\right) + \\left(A\\left(s, a, \\theta, \\alpha\\right) - \\frac{1}{|\\mathcal{A}|}\\sum\\_{a'}A\\left(s, a'; \\theta, \\alpha\\right)\\right) $$\r\n\r\nThis formulation is chosen for identifiability so that the advantage function has zero advantage for the chosen action, but instead of a maximum we use an average operator to increase the stability of the optimization.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Dueling Network Architectures for Deep Reinforcement Learning","paper":"/paper/dueling-network-architectures-for-deep","first_author":"Ziyu Wang","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/dueling-network-architectures-for-deep"},"source":{"url":"http://arxiv.org/abs/1511.06581v3","title":"Dueling Network Architectures for 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":23,"archive_num_papers":23,"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":null,"title":"Active search and coverage using point-cloud reinforcement learning","date":"2023-12-18","arxiv_id":"2312.11410","n_code_links":0,"syntology":null},{"paper":null,"title":"Deep Reinforcement Learning for Artificial Upwelling Energy Management","date":"2023-08-20","arxiv_id":"2308.10199","n_code_links":0,"syntology":null},{"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":"/paper/a-coevolutionairy-approach-to-deep-multi","title":"A coevolutionary approach to deep multi-agent reinforcement learning","date":"2021-04-12","arxiv_id":"2104.05610","n_code_links":1,"syntology":null},{"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 State Representation Dueling Network for Deep Reinforcement Learning","date":"2020-12-24","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/resolving-implicit-coordination-in-multi","title":"Resolving Implicit Coordination in Multi-Agent Deep Reinforcement Learning with Deep Q-Networks & Game Theory","date":"2020-12-08","arxiv_id":"2012.09136","n_code_links":1,"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/qplex-duplex-dueling-multi-agent-q-learning","title":"QPLEX: Duplex Dueling Multi-Agent Q-Learning","date":"2020-08-03","arxiv_id":"2008.01062","n_code_links":6,"syntology":{"ran":2,"of":4,"unverified":2,"pointer_only":0}},{"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":"Balancing a CartPole System with Reinforcement Learning -- A Tutorial","date":"2020-06-08","arxiv_id":"2006.04938","n_code_links":0,"syntology":null},{"paper":null,"title":"To Combine or Not To Combine? 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