kaal:position:2026-07-31-7758
Disentangling Successor Features for Coordination in Multi-agent Reinforcement Learning presents the following source proposition: This challenge is especially prevalent in unstructured tasks with sparse rewards and many agents. This proposition is pertinent to Kaal's source-bound 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 proposed response is a qualification: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.
economicsempirical-evidencehistorical-responsescholarly-literaturecrossref