At present, the research on the dynamics of cooperative behavior of agents under reinforcement learning mechanism either assumes that agents have global interaction, that is, agents interact with all other agents in the population, or directly study the influence of relevant factors on cooperation evolution based on the local interaction in a network structure. It neglects to formally study how the limitation of agents that only interact with local agents affects their strategy choice. Thus, in this paper, we study the cooperative behavior of agents in a typical social decision-making environment with conflicts between individual interests and collective interests. On the one hand, a programmed game model in game theory, namely, prisoner’s dilemma game, is used to capture the essence of real-world dilemmas. On the other hand, the effects of local and global strategy learning on the cooperative evolution of agents are investigated separately, and the nature of spatial reciprocity under the reinforcement learning mechanism is found. Specifically, when there is no inherent connection between the interacting agents and the learning agents within the system, the network structure has a limited effect on promoting cooperation. It is only when there is an overlap between the interacting agents and the learning agents that the spatial reciprocity effect observed in the traditional evolutionary game theory can be fully realized.
Skip Nav Destination
Article navigation
February 2025
Research Article|
February 03 2025
Spatial reciprocity under reinforcement learning mechanism
Lu Wang
;
Lu Wang
(Visualization, Writing – original draft, Writing – review & editing)
1
School of Manufacturing Science and Engineering, Southwest University of Science and Technology
, Mianyang, Sichuan 621000, China
Search for other works by this author on:
Xiaoqiu Shi
;
Xiaoqiu Shi
(Data curation, Formal analysis, Writing – review & editing)
1
School of Manufacturing Science and Engineering, Southwest University of Science and Technology
, Mianyang, Sichuan 621000, China
2Mianyang Science and Technology City Intelligent Manufacturing Industry Technology Innovation Institute, Mianyang, Sichuan 621000, China
Search for other works by this author on:
Yang Zhou
Yang Zhou
a)
(Conceptualization, Supervision, Validation)
3
Engineering Technology Center, Southwest University of Science and Technology
, Mianyang, Sichuan 621000, China
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
a)Author to whom correspondence should be addressed: [email protected]
Chaos 35, 023103 (2025)
Article history
Received:
November 04 2024
Accepted:
January 11 2025
Citation
Lu Wang, Xiaoqiu Shi, Yang Zhou; Spatial reciprocity under reinforcement learning mechanism. Chaos 1 February 2025; 35 (2): 023103. https://doi.org/10.1063/5.0246843
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
25
Views
Citing articles via
Response to music on the nonlinear dynamics of human fetal heart rate fluctuations: A recurrence plot analysis
José Javier Reyes-Lagos, Hugo Mendieta-Zerón, et al.
Reliable detection of directional couplings using cross-vector measures
Martin Brešar, Ralph G. Andrzejak, et al.
Synchronization in spiking neural networks with short and long connections and time delays
Lionel Kusch, Martin Breyton, et al.
Related Content
Effects of value-driven social learning on cooperation in the prisoner’s dilemma games
Chaos (December 2024)
Swarm intelligence inspired cooperation promotion and symmetry breaking in interdependent networked game
Chaos (April 2019)
Asymmetry of individual activity promotes cooperation in the spatial prisoner’s dilemma game
Chaos (September 2023)
Heterogeneous indirect reciprocity promotes the evolution of cooperation in structured populations
Chaos (December 2018)
Coevolution of relationship and interaction in cooperative dynamical multiplex networks
Chaos (February 2024)