Active offline policy selection
… Figure 1: Left: Offline policy selection attempts to choose the best policy from a set of policies,
… In offline policy selection (OPS) the task is to select the policy with the highest value from a …
… In offline policy selection (OPS) the task is to select the policy with the highest value from a …
Offline policy selection under uncertainty
… We formally consider offline policy selection as learning preferences over a set of policy …
evaluation (OPE) is a highly active area of research. While the original motivation for OPE …
evaluation (OPE) is a highly active area of research. While the original motivation for OPE …
Supported policy optimization for offline reinforcement learning
… We select the classic BC [36] and state-of-the-art offline RL methods as baselines. For
methods based on dynamic programming, we compare to AWAC [33], Onestep RL [2], TD3+BC [7]…
methods based on dynamic programming, we compare to AWAC [33], Onestep RL [2], TD3+BC [7]…
Mopo: Model-based offline policy optimization
… policies without any costly or dangerous active exploration. However, it is also challenging,
due to the distributional shift between the offline … can be selected from a larger set of policies. …
due to the distributional shift between the offline … can be selected from a larger set of policies. …
An offline-to-online reinforcement learning approach based on multi-action evaluation with policy extension
… policy due to the overly conservative constraints, offline RL confronts challenges in active …
Then, we consider all possible actions and select them through the action-selection calculation …
Then, we consider all possible actions and select them through the action-selection calculation …
Towards Practical Offline Reinforcement Learning: Sample Efficient Policy Selection and Evaluation
V Liu - 2024 - era.library.ualberta.ca
… In the first part of the dissertation, we study the offline policy selection problem. Policy
selection is a … We expect active research topics will be (1) to identify suitable conditions on the …
selection is a … We expect active research topics will be (1) to identify suitable conditions on the …
A survey on offline reinforcement learning: Taxonomy, review, and open problems
RF Prudencio, MROA Maximo…�- IEEE Transactions on�…, 2023 - ieeexplore.ieee.org
… in offline RL finding a better policy than the one used to generate the data. Although selecting
the … ]) can work well in the offline RL setting since we can avoid active data collection and …
the … ]) can work well in the offline RL setting since we can avoid active data collection and …
A criterion for selecting the appropriate one from the trained models for model‐based offline policy evaluation
C Li, Y Wang, ZM Ma, Y Liu�- CAAI Transactions on Intelligence�…, 2024 - Wiley Online Library
… offline datasets, thereby disallowing the agent from actively … A challenge encountered
within Offline RL is offline policy … performance of policies relying exclusively on offline data [5, …
within Offline RL is offline policy … performance of policies relying exclusively on offline data [5, …
Showing your offline reinforcement learning work: Online evaluation budget matters
V Kurenkov, S Kolesnikov�- International Conference on�…, 2022 - proceedings.mlr.press
… Training and evaluation of deep offline RL algorithms is still in active development, and …
As the EOP incorporates offline policy selection, we can also use it to compare how well OPS …
As the EOP incorporates offline policy selection, we can also use it to compare how well OPS …
Mild policy evaluation for offline actor–critic
… offline AC … a mild policy evaluation (MPE) by constraining the difference between the $Q$
values of actions supported by the target policy and those of actions contained within the offline …
values of actions supported by the target policy and those of actions contained within the offline …