Sammendrag
Deep reinforcement learning has led to numerous
notable results in robotics. However, deep neural networks
(DNNs) are unintuitive, which makes it difficult to understand
their predictions and strongly limits their potential for realworld
applications due to economic, safety, and assurance
reasons. To remedy this problem, a number of explainable AI
methods have been presented, such as SHAP and LIME, but
these can be either be too costly to be used in real-time robotic
applications or provide only local explanations. In this paper,
the main contribution is the use of a linear model tree (LMT)
to approximate a DNN policy, originally trained via proximal
policy optimization(PPO), for an autonomous surface vehicle
with five control inputs performing a docking operation. The
two main benefits of the proposed approach are: a) LMTs are
transparent which makes it possible to associate directly the
outputs (control actions, in our case) with specific values of the
input features, b) LMTs are computationally efficient and can
provide information in real-time. In our simulations, the opaque
DNN policy controls the vehicle and the LMT runs in parallel
to provide explanations in the form of feature attributions. Our
results indicate that LMTs can be a useful component within
digital assurance frameworks for autonomous ships.
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