Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms

arXiv:2607.07769v1 Announce Type: new Abstract: Starting from the utilization of deep neural networks to approximate the state-action value function that led to winning one of the most challenging games, to algorithmic advancements that allowed solving problems without even explicitly stating the rules of the challenge at hand, reinforcement learning research has been the center of remarkable scientific progress for the past decade. In this paper, we focus on the key ingredients of this research progress and we analyze the canonical evaluation and design paradigms in reinforcement learning. We

Source: arXiv cs.LG — read the full report at the original publisher.

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