ACSAC adaptively selects action chunk sizes via a causal Transformer Q-network in actor-critic RL, proves the Bellman operator is a contraction, and reports state-of-the-art results on long-horizon manipulation tasks.
Decoupled q-chunking
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Adaptive Q-Chunking selects optimal action chunk sizes at each state via normalized advantage comparisons to outperform fixed chunk sizes in offline-to-online RL on robot benchmarks.
citing papers explorer
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ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network
ACSAC adaptively selects action chunk sizes via a causal Transformer Q-network in actor-critic RL, proves the Bellman operator is a contraction, and reports state-of-the-art results on long-horizon manipulation tasks.
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Adaptive Q-Chunking for Offline-to-Online Reinforcement Learning
Adaptive Q-Chunking selects optimal action chunk sizes at each state via normalized advantage comparisons to outperform fixed chunk sizes in offline-to-online RL on robot benchmarks.