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cs.LG2022
Confident Approximate Policy Iteration for Efficient Local Planning in -realizable MDPs
Gellért Weisz, András György, Tadashi Kozuno +1
We consider approximate dynamic programming in -discounted Markov decision processes and apply it to approximate planning with linear value-function approximation. Our first con…
cs.LG2022★ 3 cited
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
Han Wang, Archit Sakhadeo, Adam White +7
The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, te…