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cs.LG2026
Ergodicity in reinforcement learning
Dominik Baumann, Erfaun Noorani, Arsenii Mustafin +5
In reinforcement learning, we typically aim to optimize the expected value of the sum of rewards an agent collects over a trajectory. However, if the process generating these rewar…
cs.LG2025
Safe exploration in reproducing kernel Hilbert spaces
Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön +1
Popular safe Bayesian optimization (BO) algorithms learn control policies for safety-critical systems in unknown environments. However, most algorithms make a smoothness assumption…
cs.LG2024
PACSBO: Probably approximately correct safe Bayesian optimization
Abdullah Tokmak, Thomas B. Schön, Dominik Baumann
Safe Bayesian optimization (BO) algorithms promise to find optimal control policies without knowing the system dynamics while at the same time guaranteeing safety with high probabi…