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Everardo Gonzalez

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAMAS Conference 2025 Conference Paper

Influence Based Reward Shaping in Multiagent Systems

Everardo Gonzalez

Learning based approaches work well to coordinate multiagent systems for a broad range of applications. A key challenge in multiagent learning is that the system reward captures the performance of many agents, making it difficult to determine which agents’ actions were helpful. Reward shaping helps address this challenge by isolating the direct impact of an agent’s actions. However, when an agent’s impact is indirect - such as influencing other teammates then existing approaches struggle. Influence based reward shaping addresses indirect impacts by rewarding an agent based on not just its own actions, but also the actions of agents it influenced. Preliminary results demonstrate that this approach leads to better coordination in a guidance mission where leaders must learn to guide followers to points of interest.

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AAMAS Conference 2024 Conference Paper

Indirect Credit Assignment in a Multiagent System

Everardo Gonzalez
Siddarth Viswanathan
Kagan Tumer

Learning in a multiagent system requires structural credit assignment to distill system performance into agent-specific feedback. Fitness shaping methods largely isolate agent credit, but struggle when an agent’s actions do not directly affect system feedback. This work introduces D-Indirect, a fitness shaping method that gives credit for both direct actions and actions that have an indirect impact on the system’s performance. We demonstrate the effectiveness of D-Indirect in a simulated shepherding scenario and our results show that learning with D-Indirect significantly outperforms learning with the standard difference evaluation and the system evaluation when agents indirectly impact system performance.

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AAMAS Conference 2022 Conference Paper

Influencing Emergent Self-Assembled Structures in Robotic Collectives Through Traffic Control

Everardo Gonzalez
Lucie Houel
Radhika Nagpal
Melinda Malley

Multiagent self-assembly allows collectives to reach areas otherwise inaccessible to any particular agent. However, the coordination of this collective is not trivial, so each agent’s position in the structure is usually determined apriori. In our approach, we take inspiration from army ants and use a simulated model of the Eciton Robotica robot [4] to form emergent structures with bio-inspired local rules. We demonstrate that by coupling this with traffic control, we can induce the formation of a structure and control certain characteristics without pre-computed paths or central coordination.

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