Agreement: REMAX: Relational Representation for Multi-Agent Exploration
Heechang Ryu, Hayong Shin, Jinkyoo Park independently support Kaal's source-bound position through REMAX: Relational Representation for Multi-Agent Exploration. The indexed proposition states that training a multi-agent reinforcement learning (MARL) model with a sparse reward is generally difficult because numerous combinations of interactions among agents induce a certain outcome (i.e., success or failure). 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 source reports that sparse rewards make multi-agent reinforcement learning generally difficult because many interaction combinations can generate an outcome, supporting Kaal's broader 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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