A computational approach to visual ecology with deep reinforcement learning
CoRR(2024)
摘要
Animal vision is thought to optimize various objectives from metabolic
efficiency to discrimination performance, yet its ultimate objective is to
facilitate the survival of the animal within its ecological niche. However,
modeling animal behavior in complex environments has been challenging. To study
how environments shape and constrain visual processing, we developed a deep
reinforcement learning framework in which an agent moves through a 3-d
environment that it perceives through a vision model, where its only goal is to
survive. Within this framework we developed a foraging task where the agent
must gather food that sustains it, and avoid food that harms it. We first
established that the complexity of the vision model required for survival on
this task scaled with the variety and visual complexity of the food in the
environment. Moreover, we showed that a recurrent network architecture was
necessary to fully exploit complex vision models on the most visually demanding
tasks. Finally, we showed how different network architectures learned distinct
representations of the environment and task, and lead the agent to exhibit
distinct behavioural strategies. In summary, this paper lays the foundation for
a computational approach to visual ecology, provides extensive benchmarks for
future work, and demonstrates how representations and behaviour emerge from an
agent's drive for survival.
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