Given a speci c problem in a domain, a standard reinforcement learner learns the optimal policy specifying the best action to take in each possible state for.
When given several problems to solve in some do- main, a standard reinforcement learner learns an op- timal policy from scratch for each problem. This.
[PDF] Learning State Features from Policies to Bias Exploration in ...
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This research is sponsored in part by the Defense Advanced Research Projects. Agency (DARPA) and the Air Force Research Laboratory (AFRL) under agreement.
Learning State Features from Policies to. Bias Exploration in Reinforcement Learning. Bryan Singer and Manuela Veloso. Computer Science Department. Carnegie ...
When given several problems to solve in some domain, a standard reinforcement learner learns an optimal policy from scratch for each problem.
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A method to bias exploration through previous problem solutions, which is shown to speed up learning on new problems, and results within the complex domain ...
Abstract: "When given several problems to solve in some domain, a standard reinforcement learner learns an optimal policy from scratch for each problem.
Learning state features from policies to bias exploration in reinforcement learning. Authors: Bryan Singer. Bryan Singer. View Profile. , Manuela Veloso.
Oct 12, 2023 · Abstract:It is desirable for policies to optimistically explore new states and behaviors during online reinforcement learning (RL) or ...
Jun 26, 2023 · If the state representation is biased, the agent may learn to behave in a way that is not optimal for the real-world environment. For example, ...