Online, off-policy prediction. A learning agent is set the task of evaluating certain states (or state/action pairs) from the perspective of an arbitrary fixed target policy π (which must be defined to the agent), and learns from observation data as it arrives.
Feb 9, 2019
Nov 6, 2018 · Abstract:This paper investigates the problem of online prediction learning, where learning proceeds continuously as the agent interacts with ...
[PDF] Online Off-policy Prediction Sina Ghiassian - Rich Sutton
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In this dissertation, we study online off-policy temporal-difference learning algorithms, a class of reinforcement learning algorithms that can learn predic ...
Jun 10, 2024 · In this article, we empirically compare 11 off-policy prediction learning algorithms with linear function approximation on three small tasks.
An empirical study of off-policy prediction methods in two challenging microworlds, focusing on 1- methods that use computation linear in the number of ...
In this dissertation, we study online off-policy temporal-difference learning algorithms, a class of reinforcement learning algorithms...
Jan 28, 2022 · Off-policy allows data to originate from older versions of the policy or completely different policies.
Jun 26, 2024 · In off-policy learning, the agent learns about a different policy than the one being executed. To account for the difference importance sampling ...
Jun 14, 2022 · Abstract:Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment.
In this work, we investigate the use of resampling for online off-policy prediction for known, un- changing target and behavior policies. We first introduce ...