Explaining the Behaviour of Reinforcement Learning Agents in a Multi-Agent Cooperative Environment Using Policy Graphs Article Swipe
Marc Vila
,
Dmitry Gnatyshak
,
Adrián Tormos
,
Víctor Giménez-Ábalos
,
Sergio Álvarez-Napagao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202401.1421.v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202401.1421.v1
The adoption of algorithms based on Artificial Intelligence (AI) has been rapidly increasing during the last years. However, some aspects of AI techniques are under heavy scrutiny. For instance, in many use cases, it is not clear whether the decisions of an algorithm are well-informed and conforming to human understanding. Having ways to address these concerns is crucial in many domains, especially whenever humans and intelligent (physical or virtual) agents must cooperate in a shared environment. In this paper, we introduce an application of an explainability method based on the creation of a Policy Graph (PG) based on discrete predicates that represent and explain a trained agent’s behaviour in a multi-agent cooperative environment. We show that from these policy graphs, policies for surrogate interpretable agents can be automatically generated. These policies can be used to measure the reliability of the explanations enabled by the PGs, through a fair behavioural comparison between the original opaque agent and the surrogate one. The contributions of this paper represent the first application of policy graphs in the context of explaining agent behaviour in collaborative multi-agent scenarios and presents experimental results that sets this kind of scenario apart from previous application in single-agent scenarios: when requiring collaborative behaviour, predicates that allow representing observations about the other agents are crucial to replicate the opaque agent’s behaviour and increase the reliability of explanations.
Related Topics
Concepts
Computer science
Scrutiny
Replicate
Context (archaeology)
Reliability (semiconductor)
Artificial intelligence
Reinforcement learning
Intelligent agent
Multi-agent system
Agent-based model
Machine learning
Mathematics
Paleontology
Biology
Quantum mechanics
Power (physics)
Political science
Law
Statistics
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202401.1421.v1
- https://www.preprints.org/manuscript/202401.1421/v1/download
- OA Status
- green
- Cited By
- 1
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391031846
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391031846Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202401.1421.v1Digital Object Identifier
- Title
-
Explaining the Behaviour of Reinforcement Learning Agents in a Multi-Agent Cooperative Environment Using Policy GraphsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-19Full publication date if available
- Authors
-
Marc Vila, Dmitry Gnatyshak, Adrián Tormos, Víctor Giménez-Ábalos, Sergio Álvarez-NapagaoList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202401.1421.v1Publisher landing page
- PDF URL
-
https://www.preprints.org/manuscript/202401.1421/v1/downloadDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.preprints.org/manuscript/202401.1421/v1/downloadDirect OA link when available
- Concepts
-
Computer science, Scrutiny, Replicate, Context (archaeology), Reliability (semiconductor), Artificial intelligence, Reinforcement learning, Intelligent agent, Multi-agent system, Agent-based model, Machine learning, Mathematics, Paleontology, Biology, Quantum mechanics, Power (physics), Political science, Law, Statistics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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