%2C exploiting environmental symmetries can significantly enhance efficiency%2C robustness%2C and performance. However%2C ensuring that the deep RL policy and value networks are respectively equivariant and invariant to exploit these symmetries is a substantial challenge. Related works try to design networks that are equivariant and invariant by construction%2C limiting them to a very restricted library of components%2C which in turn hampers the expressiveness of the networks. This paper proposes a method to construct equivariant policies and invariant value functions without specialized neural network components%2C which we term equivariant ensembles. We further add a regularization term for adding inductive bias during training. In a map-based path planning case study%2C we show how equivariant ensembles and regularization benefit sample efficiency and performance.?quality=80&w=1200)
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