Augmenting Reinforcement Learning to Enhance Cooperation in the Iterated Prisoner's Dilemma

Reinforcement learning algorithms applied to social dilemmas sometimes struggle with converging to mutual cooperation against like-minded partners, particularly when utilising greedy behavioural selection methods. Recent research has demonstrated how affective cognitive mechanisms, such as mood and emotion, might facilitate increased rates of mutual cooperation when integrated with these algorithms. This research has, thus far, primarily utilised mobile multi-agent frameworks to demonstrate this relationship - where they have also identified interaction structure as a key determinant of the emergence of cooperation. Here, we use a deterministic, static interaction structure to provide deeper insight into how a particular moody reinforcement learner might encourage the evolution of cooperation in the Iterated Prisoner's Dilemma. In a novel grid environment, we both replicated original test parameters and then varied the distribution of agents and the payoff matrix. We found that behavioural trends from past research were present (with suppressed magnitude), and that the proportion of mutual cooperations was heightened when both the influence of mood and the cooperation index of the payoff matrix chosen increased. Changing the proportion of moody agents in the environment only increased mutual cooperations by virtue of introducing cooperative agents to each other.