Controlling cooperative behaviors in real-time environments using reinforcement learning
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- dc.contributor.author Knudsen, James
- dc.date.accessioned 2025-02-18T18:04:22Z
- dc.date.available 2025-02-18T18:04:22Z
- dc.date.issued 2024
- dc.description Treball fi de màster de: Master in Cognitive Systems and Interactive Media Supervisor: Martí Sánchez-Fibla
- dc.description.abstract The research presented in this Master’s Thesis expands on previous research in the Artificial Intelligence (AI) domain of cooperative, deep, multi-agent reinforcement learning (MARL) in a common-pool resource environment. Specifically, we expand on the Forest Fire Model (FFM) presented in Cooperative Control of Environmental Extremes by Artificial Intelligent Agents. Previous research contributes an improved understanding of emergent cooperative behaviors for AI agents through the lens of game-theoretic concepts of equilibria (in the context of ecological engineering). This work presented in this thesis focuses on increasing realism in the FFM simulation by modeling the probability of ignition of fires inspired by real-world data. While the original research kept fire ignition as probabilistically constant, here we explore how periods of stress and abundance (i.e. seasonal fire impacts) guide agent behaviors. Notably, this research shows that the duration of organizational behavior and the degree to which it occurs can be controlled through manipulation of probabilistic occurrence of negative rewards - explored here as dynamic probabilistic fire functions. Understanding and controlling cooperative behaviors provides relevant solutions for addressing resource sharing among self-interested parties and serves as foundational work for climate change oriented solutions.
- dc.identifier.uri http://hdl.handle.net/10230/69631
- dc.language.iso eng
- dc.rights Attribution-NonCommercial- NoDerivs 3.0 Spain
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/es/deed
- dc.subject.keyword Artificial Intelligence
- dc.subject.keyword Deep Reinforcement Learning
- dc.subject.keyword Multi-Agent Reinforcement Learning
- dc.subject.keyword Common Pool Resource
- dc.subject.keyword Ecological Engineering
- dc.subject.keyword AI Agent Behavior
- dc.subject.keyword Cooperative Behaviors
- dc.subject.keyword Cooperative Control
- dc.title Controlling cooperative behaviors in real-time environments using reinforcement learning
- dc.type info:eu-repo/semantics/masterThesis