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Brahim Driss

2 accepted papers

2026

Performative Policy Gradient: Optimality in Performative Reinforcement Learning

ICML 2026poster

Post-deployment machine learning algorithms often influence the environments they act in, and thus *shift* the underlying dynamics that the standard reinforcement learning (RL) methods ignore. While designing optimal algorithms in this *performative* setting has recently been studied in supervised l…

Cited by 0SourceScholar
2025

Online Robust Reinforcement Learning Through Monte-Carlo Planning

ICML 2025poster

Monte Carlo Tree Search (MCTS) is a powerful framework for solving complex decision-making problems, yet it often relies on the assumption that the simulator and the real-world dynamics are identical. Although this assumption helps achieve the success of MCTS in games like Chess, Go, and Shogi, the…

Cited by 0SourcePDFScholar