Abstract simulators can be grounded to real tasks by making their dynamics history-dependent and correcting them with real data, enabling RL policy transfer.
Outracing champion gran turismo drivers with deep reinforcement learning,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A bi-level game-theoretic optimal control plus reinforcement learning framework enables competitor-aware energy management and pit-stop scheduling that exploits aerodynamic drafting in simulated electric endurance races.
citing papers explorer
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Abstract Sim2Real through Approximate Information States
Abstract simulators can be grounded to real tasks by making their dynamics history-dependent and correcting them with real data, enabling RL policy transfer.
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Competitor-aware Race Management for Electric Endurance Racing
A bi-level game-theoretic optimal control plus reinforcement learning framework enables competitor-aware energy management and pit-stop scheduling that exploits aerodynamic drafting in simulated electric endurance races.