Feb 1, 2023 · Abstract: We study the finite-horizon offline reinforcement learning (RL) problem. Since actions at any state can affect next-state ...
Feb 12, 2024 · We study the finite-horizon offline reinforcement learning (RL) problem, focusing on algorithms that adapt to instance hardness.
We develop a flexible and general method called selective uncertainty propagation for confidence interval construction that adapts to the hardness of the ...
many real-world RL instances lies between the two regimes. We develop a flexible and general method called selective uncertainty propagation for confidence ...
Feb 9, 2023 · Bibliographic details on Selective Uncertainty Propagation in Offline RL.
Dec 19, 2024 · The selective uncertainty propagation approach bridges an important gap between simple and complex learning scenarios, though additional work ...
This is a collection of research and review papers for offline reinforcement learning (offline rl). Feel free to star and fork.
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Nov 15, 2024 · This work addresses the issue of sampling for uncertainty propagation that is the standard practice in offline RL and identifies the high variance of sampling- ...
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[PDF] Uncertainty-Based Offline Reinforcement Learning with ...
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Offline reinforcement learning (offline RL), which aims to find an optimal policy from a previously collected static dataset, bears algorithmic difficulties ...
Jun 6, 2024 · Current approaches to model-based offline Reinforcement Learning (RL) often incorporate uncertainty-based reward penalization to address the ...