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← Authors

Tamer Basar

24 papers · 2018–2025 · 6 conferences · across top CS/AI conferences

Achievements

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+12 more ↓ 🌍 Conference Polyglot (6) 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🏃 Academic Marathon (7)
🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (12) 🌍 Conference Polyglot (6) 🤝 Dynamic Duo (17) 🔬 Deep Specialist (12) 🏆 Keyword Champion (2) 🚀 Conference Pioneer 🗃️ Keyword Collector (81) 📈 Trend Setter ⚡ Prolific Year (7) 🔥 Unstoppable (8) 💎 Century Club (24)

Conferences

NIPS (12) L4DC (5) ICML (3) JMLR (2) AAAI (1) AISTATS (1)

Top co-authors

Kaiqing Zhang (17) Weichao Mao (7) Chen Wang (3) Bin Hu (3) Xiangyuan Zhang (3) Hubertus Franke (3) Haoran Qiu (3) Zbigniew Kalbarczyk (3) Lin Yang (2) Alec Koppel (2)

Keywords

multi-agent reinforcement learning (12) markov game (6) sample complexity (6) nash equilibrium (6) policy optimization (6) zero-sum game (5) reinforcement learning (4) mean-field game (3) policy gradient (3) robust control (3) function approximation (2) risk-sensitive control (2) coarse correlated equilibrium (2) constrained optimization (2) primal-dual method (2) model-based reinforcement learning (2) upper confidence bound (2) natural policy gradient (2) convergence guarantee (2) decentralized learning (2)

Papers

Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs JMLR 2025 $\widetilde{O}(T^{-1})$ Convergence to (coarse) correlated equilibria in full-information general-sum Markov games L4DC 2024 Controlgym: Large-scale control environments for benchmarking reinforcement learning algorithms L4DC 2024 Robust cooperative multi-agent reinforcement learning: A mean-field type game perspective L4DC 2024 A Reinforcement Learning Look at Risk-Sensitive Linear Quadratic Gaussian Control L4DC 2023 Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity NIPS 2023 Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity JMLR 2023 Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path AISTATS 2023 On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning ICML 2022 A Mean-Field Game Approach to Cloud Resource Management with Function Approximation NIPS 2022 Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning AAAI 2021 Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity NIPS 2021 Decentralized Q-learning in Zero-sum Markov Games NIPS 2021 Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs ICML 2021 POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis NIPS 2020 Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity NIPS 2020 On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems NIPS 2020 Robust Multi-Agent Reinforcement Learning with Model Uncertainty NIPS 2020 Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes NIPS 2020 Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence L4DC 2020 An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods NIPS 2020 Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games NIPS 2019 Non-Cooperative Inverse Reinforcement Learning NIPS 2019 Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents ICML 2018