Boosting deep Reinforcement Learning using pretraining with Logical Options

Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed these challenges by encoding sparse objectives along with aligned plans. However, purely symbolic architectures are complex to scale and difficult to apply to continuous settings. Hence, we propose a hybrid approach, inspired by humans' ability to acquire new skills. We use a two-stage framework that injects symbolic structure into neural-based

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ethics.ai (6 March 2026), “Boosting deep Reinforcement Learning using pretraining with Logical Options,” evidence record 7581, https://ethics.ai/record/7581 (originally published by arXiv).

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