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Offline Actor-Critic for Large Models by ai.addict.contentstrategist is a document available to read on EtoBox.

The document presents a scalable offline actor-critic reinforcement learning (RL) approach, demonstrating that it can effectively utilize large models, such as transformers, to learn from sub-optimal data across multiple control tasks. The authors introduce the Perceiver-Actor-Critic (PAC) model, which combines offline RL with behavior cloning, allowing for improved performance on continuous control benchmarks. This work highlights the potential of offline RL as a viable alternative to traditional behaviora

Author

ai.addict.contentstrategist

Language

EN