Search Results for author:
Found 3 papers, 1 papers with code
Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks
Visual navigation requires a whole range of capabilities.
Benchmarks for Physical Reasoning AI
Therefore, we aim to offer an overview of existing benchmarks and their solution approaches and propose a unified perspective for measuring the physical reasoning capacity of AI systems.
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison
These insights can inform future development of interpretable representation learning approaches for non-visual observations and advance the use of RL solutions in real-world scenarios.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
