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ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network
Long-horizon, sparse-reward tasks pose a fundamental challenge for reinforcement learning, since single-step TD learning suffers from bootstrapping error accumulation across successive Bellman updates. Actor-critic methods with action chunking address this by operating over temporally extended actio
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