AI RESEARCH
Efficiency of Parallel and Restart Exploration Strategies in Model Free Stochastic Simulations
arXiv CS.LG
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ArXi:2503.03565v3 Announce Type: replace-cross We analyze the efficiency of parallelization and restart mechanisms for stochastic simulations in model-free settings, where the underlying system dynamics are unknown. Such settings are common in Reinforcement Learning (RL) and rare event estimation, where standard variance-reduction techniques like importance sampling are inapplicable. Focusing on the challenge of reaching rare states under a finite computational budget, we model exploration via random walks and L\'evy processes.