Bruno Scherrer

19 papers · 2008–2020 · 5 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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🗺️ Taxonomy Completionist (24) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird

🔬 Deep Specialist (10) 🏆 Keyword Champion (4) 🧭 Keyword Pioneer 💎 Century Club · Bronze 🔥 Unstoppable · Bronze 🚀 Conference Pioneer 🗃️ Keyword Collector (76) 📈 Trend Setter

Venues

ICML (7) NeurIPS (6) JMLR (3) AISTATS (2) AAAI (1)

Top co-authors

Matthieu Geist (6) Olivier Pietquin (6) Bilal Piot (3) Julien Pérolat (3) Yonathan Efroni (3) Shie Mannor (3) Gal Dalal (3) Boris Lesner (3) Nino Vieillard (2) Mohammad Ghavamzadeh (2)

Research topics

Reinforcement Learning (19) Machine Learning (17) Methods (17) Optimization & Theory (13) Learning Types · Machine Learning (12) Applications (7) Mathematics & Optimization (5) Optimization (5) Artificial Intelligence (4) Core AI (4) Deep Learning (1) Learning Types · Deep Learning (1)

Keywords

markov decision process (9) reinforcement learning (9) policy iteration (9) value iteration (8) approximate dynamic programming (5) discount factor (4) value function approximation (4) markov game (3) non-stationary policy (3) value function (2) optimal control (2) policy optimization (2) eligibility trace (2) game ai (2) markov decision processes (2) zero-sum game (2) policy learning (2) policy improvement (2) approximate policy iteration (2) iteration complexity (2)

Papers

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning NeurIPS 2020 Momentum in Reinforcement Learning AISTATS 2020 A Theory of Regularized Markov Decision Processes ICML 2019 How to Combine Tree-Search Methods in Reinforcement Learning AAAI 2019 Beyond the One-Step Greedy Approach in Reinforcement Learning ICML 2018 Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning NeurIPS 2018 On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games AISTATS 2016 Softened Approximate Policy Iteration for Markov Games ICML 2016 Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games ICML 2015 Approximate Modified Policy Iteration and its Application to the Game of Tetris JMLR 2015 Non-Stationary Approximate Modified Policy Iteration ICML 2015 On the Rate of Convergence and Error Bounds for LSTD(λ) ICML 2015 Approximate Policy Iteration Schemes: A Comparison ICML 2014 Off-policy Learning With Eligibility Traces: A Survey JMLR 2014 Approximate Dynamic Programming Finally Performs Well in the Game of Tetris NeurIPS 2013 Improved and Generalized Upper Bounds on the Complexity of Policy Iteration NeurIPS 2013 Performance Bounds for λ Policy Iteration and Application to the Game of Tetris JMLR 2013 On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes NeurIPS 2012 Biasing Approximate Dynamic Programming with a Lower Discount Factor NeurIPS 2008