The theoretical Evaluation demonstrates that EDIS exhibits lessened suboptimality in comparison with exclusively making use of online details or right reusing offline info. EDIS is usually a plug-in method and will be combined with present strategies in offline-to-on line RL location. By implementing EDIS to off-the-shelf procedures Cal-QL and IQL, we observe a notable twenty% typical enhancement in empirical performance on MuJoCo, AntMaze, and Adroit environments. Code is out there at .
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Figure seven: Examples of failure circumstances compared to the gradient ascent technique. Textual content demonstrates predicted response charge from the in just-architecture validator.
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