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title
Offline Reinforcement Learning as One Big Sequence Modeling Problem
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Berkeley,Deep,Reinforcement,Learning,Computer Science,Machine,Artificial,Intelligence
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Reinforcement Learning as One Big Sequence Modeling Problem Offline Reinforcement Learning as One Big Sequence Modeling Problem NeurIPS 2021 (spotlight) Paper Code Blog BibTex Trajectory Transformer Single-Step Model Long-horizon predictions of the Trajectory Transformer compared to those of a feedforward single-step dynamics model. Summary We view reinforcement learning as a generic sequence modeling problem and investigate how much of the usual machinery of reinforcement learning algorithms can be replaced with the tools that have found widespread use in large-scale language modeling. The core of our approach is the Trajectory Transformer, trained on sequences of states, actions, and rewards treated interchangeably, and a set of beam-search-based planners. Transformers as dynamics models Predictive dynamics models often have excellent single-step error, but poor long-horizon accuracy due to c...
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