Title:
Learning Roles: Behavioral Diversity in Robot Teams
Learning Roles: Behavioral Diversity in Robot Teams
Author(s)
Balch, Tucker
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Abstract
This paper describes research investigating behavioral specialization in
learning robot teams. Each agent is provided a common set of skills (motor
schema-based behavioral assemblages) from which it builds a task-achieving
strategy using reinforcement learning. The agents learn individually to
activate particular behavioral assemblages given their current situation and
a reward signal. The experiments, conducted in robot soccer simulations,
evaluate the agents in terms of performance, policy convergence, and
behavioral diversity. The results show that in many cases, robots will
automatically diversify by choosing heterogeneous behaviors. The degree of
diversification and the performance of the team depend on the reward
structure. When the entire team is jointly rewarded or penalized (global
reinforcement), teams tend towards heterogeneous behavior. When agents
are provided feedback individually (local reinforcement), they converge
to identical policies.
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Date Issued
1997
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201228 bytes
Resource Type
Text
Resource Subtype
Technical Report