Two new arXiv papers explore advancements in actor-critic reinforcement learning algorithms. The first paper, though later withdrawn, proposed an optimal sample complexity of O(ε−2) for single-timescale actor-critic methods by using a sample buffer and momentum. The second paper introduces a novel optimistic actor-critic algorithm for low-rank MDPs that relies solely on policy evaluation, achieving improved sample complexity without computationally expensive oracles. AI
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IMPACT These papers advance theoretical understanding of reinforcement learning, potentially leading to more efficient training of agents in complex environments.
RANK_REASON Two arXiv papers present theoretical advancements in reinforcement learning algorithms.