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- W3084294947 abstract "We present a novel approach (DyNODE) that captures the underlying dynamics of a system by incorporating control in a neural ordinary differential equation framework. We conduct a systematic evaluation and comparison of our method and standard neural network architectures for dynamics modeling. Our results indicate that a simple DyNODE architecture when combined with an actor-critic reinforcement learning (RL) algorithm that uses model predictions to improve the critic's target values, outperforms canonical neural networks, both in sample efficiency and predictive performance across a diverse range of continuous tasks that are frequently used to benchmark RL algorithms. This approach provides a new avenue for the development of models that are more suited to learn the evolution of dynamical systems, particularly useful in the context of model-based reinforcement learning. To assist related work, we have made code available at this https URL ." @default.
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- W3084294947 date "2020-09-09" @default.
- W3084294947 modified "2023-09-27" @default.
- W3084294947 title "DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control." @default.
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