Adaptive patch foraging in deep reinforcement learning agents

Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biological intelligence, this behavioral optimization problem is understudied in artificial intelligence research. Patch foraging is especially amenable to study given that it has a known optimal solution, which may be difficult to discover given current techniques in deep reinforcement learning. Here, we investigate deep reinforcement learning agents in an ecological patch foraging task. For the first time, we show that machine learning agents can learn to patch forage adaptively in patterns similar to biological foragers, and approach optimal patch foraging behavior when accounting for temporal discounting. Finally, we show emergent internal dynamics in these agents that resemble single-cell recordings from foraging non-human primates, which complements experimental and theoretical work on the neural mechanisms of biological foraging. This work suggests that agents interacting in complex environments with ecologically valid pressures arrive at common solutions, suggesting the emergence of foundational computations behind adaptive, intelligent behavior in both biological and artificial agents.Comment: Published in Transactions on Machine Learning Research (TMLR). See: https://openreview.net/pdf?id=a0T3nOP9s

PREDICTORS OF HYPERTENSION

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74570/1/j.1749-6632.1978.tb25565.x.pd

Sleeping Site Selection by Agile Gibbons: The Influence of Tree Stability, Fruit Availability and Predation Risk

Primates spend a significant proportion of their lives at sleeping sites: the selection of a secure and stable sleeping tree can be crucial for individual survival and fitness. We measured key characteristics of all tree species in which agile gibbons slept, including exposure of the tree crown, root system, height, species and presence of food. Gibbons most frequently slept in Dipterocarpaceae and Fabaceae trees and preferentially chose trees taller than average, slept above the mean canopy height and showed a preference for liana-free trees. These choices could reflect avoidance of competition with other frugivores, but we argue these choices reflect gibbons prioritizing avoidance of predation. The results highlight that gibbons are actively selecting and rejecting sleeping trees based on several characteristics. The importance of the presence of large trees for food is noted and provides insight into gibbon antipredatory behaviour

Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults

Cognitive training interventions are a promising approach to mitigate cognitive deficits common in aging and, ultimately, to improve functioning in older adults. Baseline neural factors, such as properties of brain networks, may predict training outcomes and can be used to improve the effectiveness of interventions. Here, we investigated the relationship between baseline brain network modularity, a measure of the segregation of brain sub-networks, and training-related gains in cognition in older adults. We found that older adults with more segregated brain sub-networks (i.e., more modular networks) at baseline exhibited greater training improvements in the ability to synthesize complex information. Further, the relationship between modularity and training-related gains was more pronounced in sub-networks mediating "associative" functions compared with those involved in sensory-motor processing. These results suggest that assessments of brain networks can be used as a biomarker to guide the implementation of cognitive interventions and improve outcomes across individuals. More broadly, these findings also suggest that properties of brain networks may capture individual differences in learning and neuroplasticity. Trail Registration: ClinicalTrials.gov, NCT#00977418