Adaptive patch foraging in deep reinforcement learning agents
- URL: http://arxiv.org/abs/2210.08085v2
- Date: Fri, 21 Apr 2023 15:21:21 GMT
- Title: Adaptive patch foraging in deep reinforcement learning agents
- Authors: Nathan J. Wispinski, Andrew Butcher, Kory W. Mathewson, Craig S.
Chapman, Matthew M. Botvinick, Patrick M. Pilarski
- Abstract summary: We show that machine learning agents can learn to patch forage adaptively in patterns similar to biological foragers.
This work suggests that agents interacting in complex environments with ecologically valid pressures arrive at common solutions.
- Score: 4.654270325882834
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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