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This paper proposes Generalized Agent Iteration (GAI), a formal framework that describes iterative policy improvement and RSI as two cases of a single learning paradigm that rests on the classical account, makes existing systems comparable, and provides a principled basis for analyzing and designing new ones.
WarpSAC redefines off-policy RL by tailoring stabilizers to data availability, achieving up to 96.4% success rates in challenging environments.
