Title:
Reward and Diversity in Multirobot Foraging
Reward and Diversity in Multirobot Foraging
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Balch, Tucker
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Abstract
This research seeks to quantify the impact of the
choice of reward function on behavioral diversity in
learning robot teams. The methodology developed
for this work has been applied to multirobot foraging, soccer and cooperative movement. This paper
focuses specifically on results in multirobot foraging. In these experiments three types of reward are
used with Q-learning to train a multirobot team to
forage: a local performance-based reward, a global
performance-based reward, and a heuristic strategy
referred to as shaped reinforcement. Local strategies provide each agent a specific reward according
to its own behavior, while global rewards provide
all the agents on the team the same reward simultaneously. Shaped reinforcement provides a heuristic reward for an agent's action given its situation.
The experiments indicate that local performance-based rewards and shaped reinforcement generate
statistically similar results: they both provide the
best performance and the least diversity. Finally,
learned policies are demonstrated on a team of Nomadic Technologies' Nomad-150 robots.
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Date Issued
1999
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Paper