[1903.08894] Towards Characterizing Divergence in Deep Q-Learning

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. arXiv Operational Status Get status notifications via email or slack

[1903.08894] Towards Characterizing Divergence in Deep Q-Learning Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Machine Learning arXiv:1903.08894 (cs) [Submitted on 21 Mar 2019] Title: Towards Characterizing Divergence in Deep Q-Learning Authors: Joshua Achiam , Ethan Knight , Pieter Abbeel View a PDF of the paper titled Towards Characterizing Divergence in Deep Q-Learning, by Joshua Achiam and 2 other authors View PDF Abstract: Deep Q-Learning (DQL), a family of temporal differe

related reading

• [2604.21691] There Will Be a Scientific Theory of Deep Learningarxiv.org
• [2604.21691] There Will Be a Scientific Theory of Deep Learningarxiv.org
• [1709.06560] Deep Reinforcement Learning that Mattersarxiv.org
• Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewarxiv.org
• arxiv.org/pdf/1805.08522arxiv.org
• [2503.09829] SE(3)-Equivariant Robot Learning and Control: A Tutorial Surveyarxiv.org
• [2512.19428] Attention Is Not What You Needarxiv.org
• [2312.08484] Self-Play Q-learners Can Provably Collude in the Iterated Prisoner's Dilemmaarxiv.org
• [2406.04219] Multi-Agent Imitation Learning: Value is Easy, Regret is Hardarxiv.org
• DiffTaichi: Differentiable Programming for Physical Simulationarxiv.org
• [1906.09624] On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inferencearxiv.org
• [2303.04150] Evolutionary Reinforcement Learning: A Surveyarxiv.org