The limited priors required by neural networks make them the dominating choice to encode
and learn policies using reinforcement learning (RL). However, they are also black-boxes …

Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many
issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal …

Artificial agents' adaptability to novelty and alignment with intended behavior is crucial for
their effective deployment. Reinforcement learning (RL) leverages novelty as a means of …

Deep reinforcement learning agents are prone to goal misalignments. The black-box nature
of their policies hinders the detection and correction of such misalignments, and the trust …

Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for
explainable decision-making, characterized by the interpretability of symbolic policies. For …

Deep reinforcement learning (RL) has led to encouraging successes in numerous
challenging robotics applications. However, the lack of inductive biases to support logic …

The challenge in object-based visual reasoning lies in generating descriptive yet distinct
concept representations. Moreover, doing this in an unsupervised fashion requires human …

Deep reinforcement learning (RL) agents rely on shortcut learning, preventing them from
generalizing to slightly different environments. To address this problem, symbolic method …

Humans can leverage both symbolic reasoning and intuitive reactions. In contrast,
reinforcement learning policies are typically encoded in either opaque systems like neural …

Latest insights from biology show that intelligence not only emerges from the connections
between neurons but that individual neurons shoulder more computational responsibility …