Unverified50% confidenceFactExact time
Temporal-difference-based methods struggle to attribute sparse final rewards to early actions, resulting in extremely weak learning signals.
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10/21/2026
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UnverifiedStandard reinforcement learning algorithms propagate rewards based on temporal proximity rather than explicit relevance, causing the learning signal for early actions that are temporally distant but decisively important to be severely attenuated.80% similarUnverifiedIn reward function design, sparse rewards can easily cause training to stagnate, and introducing dense reward signals such as distance reduction can accelerate convergence.79% similarUnverifiedSparse rewards only provide signals when a task succeeds or fails, which leads to extremely low exploration efficiency in long-horizon, multi-stage tasks.79% similarUnverifiedIn the sampled-gradient setting, when the learning rate η decreases, the expected gain under fixed β vanishes faster than the fluctuation itself, causing the benefit of momentum to be swallowed by noise.78% similarUnverifiedThe sparse feedback nature of MMORPGs causes reward signals to be delayed and infrequent, which is unfavorable for traditional reinforcement learning77% similar
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