8 research outputs found
Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning
Humans learn by interacting with their environments and perceiving the
outcomes of their actions. A landmark in artificial intelligence has been the
development of deep reinforcement learning (dRL) algorithms capable of doing
the same in video games, on par with or better than humans. However, it remains
unclear whether the successes of dRL models reflect advances in visual
representation learning, the effectiveness of reinforcement learning algorithms
at discovering better policies, or both. To address this question, we introduce
the Learning Challenge Diagnosticator (LCD), a tool that separately measures
the perceptual and reinforcement learning demands of a task. We use LCD to
discover a novel taxonomy of challenges in the Procgen benchmark, and
demonstrate that these predictions are both highly reliable and can instruct
algorithmic development. More broadly, the LCD reveals multiple failure cases
that can occur when optimizing dRL algorithms over entire video game benchmarks
like Procgen, and provides a pathway towards more efficient progress
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups.
International Journal of Computer Vision123454-47
Fully automated leg tracking of Drosophila neurodegeneration models reveals distinct conserved movement signatures.
Some neurodegenerative diseases, like Parkinsons Disease (PD) and Spinocerebellar ataxia 3 (SCA3), are associated with distinct, altered gait and tremor movements that are reflective of the underlying disease etiology. Drosophila melanogaster models of neurodegeneration have illuminated our understanding of the molecular mechanisms of disease. However, it is unknown whether specific gait and tremor dysfunctions also occur in fly disease mutants. To answer this question, we developed a machine-learning image-analysis program, Feature Learning-based LImb segmentation and Tracking (FLLIT), that automatically tracks leg claw positions of freely moving flies recorded on high-speed video, producing a series of gait measurements. Notably, unlike other machine-learning methods, FLLIT generates its own training sets and does not require user-annotated images for learning. Using FLLIT, we carried out high-throughput and high-resolution analysis of gait and tremor features in Drosophila neurodegeneration mutants for the first time. We found that fly models of PD and SCA3 exhibited markedly different walking gait and tremor signatures, which recapitulated characteristics of the respective human diseases. Selective expression of mutant SCA3 in dopaminergic neurons led to a gait signature that more closely resembled those of PD flies. This suggests that the behavioral phenotype depends on the neurons affected rather than the specific nature of the mutation. Different mutations produced tremors in distinct leg pairs, indicating that different motor circuits were affected. Using this approach, fly models can be used to dissect the neurogenetic mechanisms that underlie movement disorders