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cs.LG2024★ 1 cited
Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents
Quentin Delfosse, Sebastian Sztwiertnia, Mark Rothermel +2
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 opt…
cs.LG2023★ 1 cited
OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments
Quentin Delfosse, Jannis Blüml, Bjarne Gregori +2
Cognitive science and psychology suggest that object-centric representations of complex scenes are a promising step towards enabling efficient abstract reasoning from low-level per…