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 "text": "Jennifer She, Jayesh K. Gupta, Mykel J. Kochenderfer independently support Kaal's source-bound position through Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning. The indexed proposition states that sparse and delayed rewards pose a challenge to single agent reinforcement learning. This bears on Kaal's claim that deep reinforcement learning demands large amounts of training data, which suggests its algorithms differ fundamentally from human learning, and learning without supervision becomes particularly hard when rewards are sparse, as they typically are in sequence generation tasks. The source independently characterizes sparse and delayed rewards as a learning challenge; Kaal's claim additionally addresses data demand and sequence generation. The response is limited to the indexed proposition and does not imply review of the full external work.",
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