DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

• URL: http://arxiv.org/abs/2009.04278v1
• Date: Wed, 9 Sep 2020 12:56:58 GMT
• Title: DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control
• Authors: Victor M. Martinez Alvarez and Rare\c{s} Ro\c{s}ca and Cristian G. F\u{a}lcu\c{t}escu
• Abstract summary: We present a novel approach that captures the underlying dynamics of a system by incorporating control in a neural ordinary differential equation framework. Results indicate that a simple DyNODE architecture when combined with an actor-critic reinforcement learning algorithm outperforms canonical neural networks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/vmartinezalvarez/DyNODE .

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