Reinforcement Learning As a Rehearsal For Swarm Foraging
Document Type
Article
Publication Date
1-1-2021
School
Computing Sciences and Computer Engineering
Abstract
Foraging in a swarm of robots has been investigated by many researchers, where the prevalent techniques have been hand-designed algorithms with parameters often tuned via machine learning. Our departure point is one such algorithm, where we replace a hand-coded decision procedure with reinforcement learning (RL), resulting in significantly superior performance. We situate our approach within the reinforcement learning as a rehearsal (RLaR) framework, that we have recently introduced. We instantiate RLaR for the foraging problem and experimentally show that a key component of RLaR—a conditional probability distribution function—can be modeled as a uni-modal distribution (with a lower memory footprint) despite evidence that it is multi-modal. Our experiments also show that the learned behavior has some degree of scalability in terms of variations in the swarm size or the environment.
Publication Title
Swarm Intelligence
Recommended Citation
Nguyen, T.,
Banerjee, B.
(2021). Reinforcement Learning As a Rehearsal For Swarm Foraging. Swarm Intelligence.
Available at: https://aquila.usm.edu/fac_pubs/19500