Modularity benefits reinforcement learning agents with competing homeostatic drives

• URL: http://arxiv.org/abs/2204.06608v1
• Date: Wed, 13 Apr 2022 18:57:55 GMT
• Title: Modularity benefits reinforcement learning agents with competing homeostatic drives
• Authors: Zack Dulberg, Rachit Dubey, Isabel M. Berwian, Jonathan D. Cohen
• Abstract summary: We investigate a biologically relevant multi-objective problem, the continual homeostasis of a set of variables, and compare a monolithic deep Q-network to a modular network with a dedicated Q-learner for each variable. We find that the modular agent: a) requires minimally determined exploration; b. has improved sample efficiency; and c. is more robust to out-of-domain perturbation.
• Score: 5.044282303487273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The problem of balancing conflicting needs is fundamental to intelligence. Standard reinforcement learning algorithms maximize a scalar reward, which requires combining different objective-specific rewards into a single number. Alternatively, different objectives could also be combined at the level of action value, such that specialist modules responsible for different objectives submit different action suggestions to a decision process, each based on rewards that are independent of one another. In this work, we explore the potential benefits of this alternative strategy. We investigate a biologically relevant multi-objective problem, the continual homeostasis of a set of variables, and compare a monolithic deep Q-network to a modular network with a dedicated Q-learner for each variable. We find that the modular agent: a) requires minimal exogenously determined exploration; b) has improved sample efficiency; and c) is more robust to out-of-domain perturbation.

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