Nothing better to do? Environment quality and the evolution of cooperation by partner choice Summary StatementPartner choice enables individuals to avoid defectors, but is seldomly observed in nonhuman animals. We show that the availability of opportunities, depending on both resources and partners, is critical.
The effects of partner choice have been documented in a large number of biological systems such as sexual markets, inter-specific mutualisms, or human cooperation. By contrast, this mechanism has never been demonstrated in a large number of intra-specific interactions in non-human animals such as collective hunts, although one would expect it to play a role as well.Here we use individual-based simulations to solve this apparent paradox. We show that the conditions for partner choice to operate are in fact restrictive. They entail that individuals can compare social opportunities and choose the best. The challenge is that social opportunities are often rare because they necessitate the co-occurrence of (i) at least one available partner, and (ii) a resource to exploit together with this partner. This has three consequences. Firstly, partner choice cannot lead to the evolution of cooperation when resources are scarce, which explains that this mechanism could never be observed in many cases of intraspecific cooperation in animals. On the other hand, partner choice can operate when partners constitute in themselves a resource, which is the case in sexual interactions and inter-specific mutualisms. Lastly, partner choice can lead to the evolution of cooperation when individuals are highly efficient at finding resources in their environment, which sheds light on the relationship between cognitive abilities and cooperation, in particular in the human species.
This paper focuses on a class of reinforcement learning problems where significant events are rare and limited to a single positive reward per episode. A typical example is that of an agent who has to choose a partner to cooperate with, while a large number of partners are simply not interested in cooperating, regardless of what the agent has to offer. We address this problem in a continuous state and action space with two different kinds of search methods: a gradient policy search method and a direct policy search method using an evolution strategy. We show that when significant events are rare, gradient information is also scarce, making it difficult for policy gradient search methods to find an optimal policy, with or without a deep neural architecture. On the other hand, we show that direct policy search methods are invariant to the rarity of significant events, which is yet another confirmation of the unique role evolutionary algorithms has to play as a reinforcement learning method.
This paper addresses the problem of learning cooperative strategies in swarm robotics. We are interested in heterogeneous swarms, in which each robot optimizes its individual gain. For some tasks, the problem is that the optimal strategy requires to cooperate and may be counter-selected in favor of a more stable but less efficient selfish strategy. To solve this problem, we introduce a mechanism of partner choice, which conditions of operation are learned. This mechanism proves surprisingly efficient, when the swarm size is large, and the duration of interactions is long. Beyond evolutionary swarm robotics, the results we present are relevant for other distributed on-line learning methods for robotics, and as a possible extension of existing evolutionary biology and social learning models.
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