Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning

• URL: http://arxiv.org/abs/2012.09156v1
• Date: Wed, 16 Dec 2020 18:47:37 GMT
• Title: Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
• Authors: Nathan O. Lambert, Albert Wilcox, Howard Zhang, Kristofer S. J. Pister, Roberto Calandra
• Abstract summary: We propose a new parametrization to supervised learning on state-action data to stably predict at longer horizons. Our results in simulated and experimental robotic tasks show that our trajectory-based models yield significantly more accurate long term predictions.
• Score: 7.194382512848327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-step ahead prediction model and using it to recursively propagate the predicted state distribution over long horizons. Unfortunately, this approach is known to compound even small prediction errors, making long-term predictions inaccurate. In this paper, we propose a new parametrization to supervised learning on state-action data to stably predict at longer horizons -- that we call a trajectory-based model. This trajectory-based model takes an initial state, a future time index, and control parameters as inputs, and predicts the state at the future time. Our results in simulated and experimental robotic tasks show that our trajectory-based models yield significantly more accurate long term predictions, improved sample efficiency, and ability to predict task reward.

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