A review of cooperation in multi-agent learning
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …
disciplines, including game theory, economics, social sciences, and evolutionary biology …
Robustness and adaptability of reinforcement learning-based cooperative autonomous driving in mixed-autonomy traffic
Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
[HTML][HTML] Mathematical frameworks for the analysis of norms
A Sontuoso - Current Opinion in Psychology, 2024 - Elsevier
Research into society's informal rules of conduct, or norms, has recently experienced a
surge, extending across multiple academic disciplines. Despite this growth, the theoretical …
surge, extending across multiple academic disciplines. Despite this growth, the theoretical …
Get it in writing: Formal contracts mitigate social dilemmas in multi-agent rl
PJK Christoffersen, AA Haupt… - arXiv preprint arXiv …, 2022 - arxiv.org
Multi-agent reinforcement learning (MARL) is a powerful tool for training automated systems
acting independently in a common environment. However, it can lead to sub-optimal …
acting independently in a common environment. However, it can lead to sub-optimal …
Formal contracts mitigate social dilemmas in multi-agent reinforcement learning
Abstract Multi-agent Reinforcement Learning (MARL) is a powerful tool for training
autonomous agents acting independently in a common environment. However, it can lead to …
autonomous agents acting independently in a common environment. However, it can lead to …
The emergence of division of labour through decentralized social sanctioning
Human ecological success relies on our characteristic ability to flexibly self-organize into
cooperative social groups, the most successful of which employ substantial specialization …
cooperative social groups, the most successful of which employ substantial specialization …
Investigating the impact of direct punishment on the emergence of cooperation in multi-agent reinforcement learning systems
N Dasgupta, M Musolesi - arXiv preprint arXiv:2301.08278, 2023 - arxiv.org
Solving the problem of cooperation is of fundamental importance to the creation and
maintenance of functional societies, with examples of cooperative dilemmas ranging from …
maintenance of functional societies, with examples of cooperative dilemmas ranging from …
Learning Optimal" Pigovian Tax" in Sequential Social Dilemmas
In multi-agent reinforcement learning, each agent acts to maximize its individual
accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect …
accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect …
Learning Fair Cooperation in Mixed-Motive Games with Indirect Reciprocity
Altruistic cooperation is costly yet socially desirable. As a result, agents struggle to learn
cooperative policies through independent reinforcement learning (RL). Indirect reciprocity …
cooperative policies through independent reinforcement learning (RL). Indirect reciprocity …
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning
How have individuals of social animals in nature evolved to learn from each other, and what
would be the optimal strategy for such learning in a specific environment? Here, we address …
would be the optimal strategy for such learning in a specific environment? Here, we address …