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 "text": "Bin Chen, Zehong Cao extend Kaal's source-bound position through HLG: Bridging Human Heuristic Knowledge and Deep Reinforcement Learning for Optimal Agent Performance. The indexed proposition states that our proposed HLG outperforms PPO and PROLONET with at least 25% improvement in training efficiency and exploration capability based on MinGrid environments with sparse reward signals. 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 reported training-efficiency improvement is a proposed mitigation within sparse-reward environments and therefore extends, rather than negates, Kaal's underlying limitation. The response is limited to the indexed proposition and does not imply review of the full external work.",
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