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Found 2 papers, 2 papers with code
Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents
Unfortunately, the black-box nature of deep neural networks impedes the inclusion of domain experts for inspecting the model and revising suboptimal policies.
OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments
In our work, we extend the Atari Learning Environments, the most-used evaluation framework for deep RL approaches, by introducing OCAtari, that performs resource-efficient extractions of the object-centric states for these games.
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