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- W2576475478 abstract "In this work, we introduce a technique that uses stories to train virtual agents to exhibit believable behavior. This technique uses a compact representation of a story to define the space of acceptable behaviors and then uses this space to assign rewards to certain world states. We show the effectiveness of our technique with a case study in a modified gridworld environment called Pharmacy World. The results show that a reinforcement learning agent using Q-learning was able to learn a policy that results in believable behavior." @default.
- W2576475478 created "2017-01-26" @default.
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- W2576475478 date "2016-01-01" @default.
- W2576475478 modified "2023-10-18" @default.
- W2576475478 title "Towards Learning From Stories: An Approach to Interactive Machine Learning." @default.
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