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"Combining Dense and Sparse Rewards to Improve Deep Reinforcement Learning ..."

Jefferson Silveira et al. (2024)

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DOI: 10.1109/CODIT62066.2024.10708505

access: closed

type: Conference or Workshop Paper

metadata version: 2024-11-12

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  - conf/codit/SilveiraMRWG24
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  - https://dblp.org/rec/conf/codit/SilveiraMRWG24
Jefferson Silveira, Kalena McCloskey, Camille Alain Rabbath, Craig Williams, Sidney Givigi:
Combining Dense and Sparse Rewards to Improve Deep Reinforcement Learning Policies in Reach-Avoid Games with Faster Evaders in Two vs. One Scenarios. CoDIT 2024: 2590-2595

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