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Table of contents
Introduction
1 Evolution of Cooperation and Partner Choice
1.1 The Evolution of Cooperation
1.1.1 Evolutionary approaches to behaviour
1.1.2 The Problem of Cooperation
1.1.3 Kin selection and indirect fitness benefits
1.1.4 Mutualism
1.1.5 Partner Choice and Biological Markets
1.1.6 Why isn’t cooperation everywhere?
1.2 Models for the Evolution of Cooperation
1.3 Models of Partner Choice
1.3.1 Population Diversity
1.3.2 The biological market in a spatialised environment
1.3.3 Competitive Helping
1.3.4 Partner choice with memory
1.3.5 Seeking Time and Interaction Time
1.3.6 Discussion on partner choice modelling
1.4 Adaptative Swarm Robotics
1.4.1 Evolutionary Robotics and Collective Systems
1.4.2 Evolutionary robotics as a Method to Understand Cooperation in Nature
1.5 Thesis objective
2 Nothing better to do? Environment quality and the evolution of cooperation by partner choice
2.1 Introduction
2.2 Methods
2.2.1 The decision-making mechanisms
2.2.2 Phenotypic variability of cooperation
2.2.3 The payoff function
2.2.4 The evolutionary algorithm
2.3 Results
2.3.1 Cooperation cannot evolve when patches are scarce
2.3.2 Cooperation cannot evolve when there are too many partners around
2.3.3 Analysis of the behaviour of “patch ranking” networks
2.4 Discussion
2.5 Supplementary Materials
3 Learning to Cooperate in a Socially Optimal Way in Swarm Robotics
3.1 Introduction
3.2 Methods
3.2.1 Environment
3.2.2 Payoff function
3.2.3 Partner Choice
3.2.4 Robotic Behaviors
3.2.5 Controller and Representation
3.2.6 Learning
3.3 Results
3.3.1 Experimental setup
3.3.2 Learning Cooperation and Population Size
3.3.3 Learning Cooperation and Interaction Length
3.3.4 Effect of Mutation Strength (Control)
3.3.5 Population Size vs Generations (Control)
3.3.6 Wandering and Relocation (Control)
3.4 Conclusion
3.5 Supplementary Materials
4 Policy Search when Significant Events are Rare: Choosing the Right Partner to Cooperate with
4.1 Introduction
4.2 Methods
4.2.1 Learning with Rare Significant Events
4.2.2 Partner Choice and Payoff Function
4.2.3 Behavioural Strategies
4.3 Parameter Settings and Algorithms
4.3.1 Proximal Policy Optimization
4.3.2 Covariance Matrix Adaptation Evolution Strategy
4.4 Results
4.4.1 Learning with always significant events
4.4.2 Learning with rare significant events
4.4.3 Analysing best policies for partner choice
4.5 Concluding Remarks
4.6 Supplementary Materials
4.6.1 Detail analysis of the agents’ reward
4.6.2 Re-evaluation performance statistical score
4.6.3 Timing
5 Conclusion
5.1 Summary
5.2 Discussion and Perspectives
