Diversity in behaviors is instrumental for robust team performance in many multiagent tasks which require agents to coordinate. Unfortunately, exhaustive search through the agents’ behavior spaces is often intractable. This paper introduces Behavior Exploration for Heterogeneous Teams (BEHT), a multi-level learning framework that enables agents to progressively explore regions of the behavior space that promote team coordination on diverse goals. By combining diversity search to maximize agent-specific rewards and evolutionary optimization to maximize the team-based fitness, our method effectively filters regions of the behavior space that are conducive to agent coordination. We demonstrate the diverse behaviors and synergies that are method allows agents to learn on a multiagent exploration problem.
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Heterogeneous Agent Coordination via Adaptive Quality Diversity and Specialization
In many real-world multiagent systems, agents must learn diverse tasks and coordinate with other agents. This paper introduces a method to allow heterogeneous agents to specialize and only learn complementary divergent behaviors needed for coordination in a shared environment. We use a hierarchical decomposition of diversity search and fitness optimization to allow agents to speciate and learn diverse temporally extended actions. Within an agent population, diversity in niches is favored. Agents within a niche compete for optimizing the higher level coordination task. Experimental results in a multiagent rover exploration task demonstrate the diversity of acquired agent behavior that promotes coordination.
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- Award ID(s):
- 1815886
- NSF-PAR ID:
- 10294970
- Date Published:
- Journal Name:
- Genetic and evolutionary computation
- ISSN:
- 1932-0175
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Agmon, N ; An, B ; Ricci, A ; Yeoh, W. (Ed.)In multiagent systems that require coordination, agents must learn diverse policies that enable them to achieve their individual and team objectives. Multiagent Quality-Diversity methods partially address this problem by filtering the joint space of policies to smaller sub-spaces that make the diversification of agent policies tractable. However, in teams of asymmetric agents (agents with different objectives and capabilities), the search for diversity is primarily driven by the need to find policies that will allow agents to assume complementary roles required to work together in teams. This work introduces Asymmetric Island Model (AIM), a multiagent framework that enables populations of asymmetric agents to learn diverse complementary policies that foster teamwork via dynamic population size allocation on a wide variety of team tasks. The key insight of AIM is that the competitive pressure arising from the distribution of policies on different team-wide tasks drives the agents to explore regions of the policy space that yield specializations that generalize across tasks. Simulation results on multiple variations of a remote habitat problem highlight the strength of AIM in discovering robust synergies that allow agents to operate near-optimally in response to the changing team composition and policies of other agents.more » « less
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