Unverified50% confidenceFactExact time
Potential-based Reward Shaping was proposed by Ng et al. in 1999: if the intermediate reward is defined as F(s,s') = γΦ(s') - Φ(s), then the optimal policy will not change due to the shaping.
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10/14/2026
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UnverifiedIf the shaping reward takes the form F(s, s') = γΦ(s') − Φ(s), then the optimal policy obtained under this shaping is exactly identical to the optimal policy of the original sparse-reward problem.76% similarUnverifiedPotential-based reward shaping guarantees that the optimal policy remains unchanged.73% similarUnverifiedPPRS (Progress Potential Reward Shaping) computes the reward for each step based on the potential increment of a potential function Φ, and is a concrete instance of the potential-based shaping framework proved by Ng et al. in 1999, satisfying reward shaping invariance.68% similarUnverifiedThe NGU algorithm superimposes external rewards with two types of intrinsic rewards: a short-term reward based on intra-episode novelty and a long-term reward based on lifetime novelty.67% similarUnverifiedReward shaping refers to adding extra, denser guiding reward signals on top of the original sparse reward to help agents learn faster and more correctly.66% similar
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