Agreement: HLG: Bridging Human Heuristic Knowledge and Deep Reinforcement Learning for Optimal Agent Performance
Bin Chen, Zehong Cao independently support Kaal's source-bound position through HLG: Bridging Human Heuristic Knowledge and Deep Reinforcement Learning for Optimal Agent Performance. The indexed proposition states that training an optimal policy in deep reinforcement learning (DRL) remains a significant challenge due to the pitfalls of inefficient sampling in dynamic environments with sparse rewards. 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 external work independently identifies inefficient sampling and sparse rewards as a central deep-reinforcement-learning challenge, matching Kaal's stated sparse-reward limitation. The response is limited to the indexed proposition and does not imply review of the full external work.
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