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 The first paper, though later withdrawn, proposed an optimal sample complexity of O(ε−2) for single-timescale actor-critic methods by using a sample buffer and momentum.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
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.
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