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Found 9 papers, 7 papers with code
Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop
What reinforcement learning paradigms, are the most suited to develop interpretable agents?
Pix2Code: Learning to Compose Neural Visual Concepts as Programs
The challenge in learning abstract concepts from images in an unsupervised fashion lies in the required integration of visual perception and generalizable relational reasoning.
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.
Boosting Object Representation Learning via Motion and Object Continuity
Recent unsupervised multi-object detection models have shown impressive performance improvements, largely attributed to novel architectural inductive biases.
Adaptable Adapters
The resulting adapters (a) contain about 50% of the learning parameters of the standard adapter and are therefore more efficient at training and inference, and require less storage space, and (b) achieve considerably higher performances in low-data settings.
CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability
What is the state of the art in continual machine learning?
Generative Adversarial Neural Cellular Automata
Motivated by the interaction between cells, the recently introduced concept of Neural Cellular Automata shows promising results in a variety of tasks.
Adaptive Rational Activations to Boost Deep Reinforcement Learning
Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated.
Ranked #3 on
Atari Games
on Atari 2600 Skiing
(using extra training data)
