A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning
CoRR(2023)
Abstract
Model-based Reinforcement Learning (MBRL) aims to make agents more
sample-efficient, adaptive, and explainable by learning an explicit model of
the environment. While the capabilities of MBRL agents have significantly
improved in recent years, how to best learn the model is still an unresolved
question. The majority of MBRL algorithms aim at training the model to make
accurate predictions about the environment and subsequently using the model to
determine the most rewarding actions. However, recent research has shown that
model predictive accuracy is often not correlated with action quality, tracing
the root cause to the objective mismatch between accurate dynamics model
learning and policy optimization of rewards. A number of interrelated solution
categories to the objective mismatch problem have emerged as MBRL continues to
mature as a research area. In this work, we provide an in-depth survey of these
solution categories and propose a taxonomy to foster future research.
MoreTranslated text
Key words
objective mismatch,reinforcement learning,solving,model-based
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