Model-Based Meta Reinforcement Learning

Author(s): Sherwin Chen

Originally published on Towards AI.

Dive into a model-based meta-RL algorithm that enables fast adaptation

Image by mrthoif0 from Pixabay

Much ink has been spilled on with model-free meta-RL in the previous article. In this article, we present a model-based meta-RL framework, proposed by Nagabandi&Clavera et al., that can adapt to changes of environment dynamics fastly. Long story short, this method learns a dynamic model that can fastly adapt to the environment changes and uses this model to do model predictive control(MPC) to take action. It is worth noting that the adaptive nature of the learned dynamic model is especially important not only in meta-learning but also in model-based RL since it alleviates the requirement of… Read the full blog for free on Medium.

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Published via Towards AI