Using Natural Language for Reward Shaping in Reinforcement Learning

IJCAI, pp. 2385-2391, 2019.

Cited by: 14|Bibtex|Views43|DOI:https://doi.org/10.24963/ijcai.2019/331
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Recent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient. A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rew...More

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