Specifying reward signals that allow agents to learn complex behaviors is a longstanding challenge in reinforcement learning.

In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.

Therefore, neuro-symbolic RL aims at creating policies that are interpretable in the first place. Unfortunately, interpretability is not explainability. To achieve both, we introduce Neurally gUided Differentiable loGic policiEs (NUDGE).

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Moreover, inconsistent bench-marking on a wide range of tasks and datasets make it difficult to assess or invigorate advances in this field.