Symmetric solution of the Bellman optimality equation for repeated harmony game
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Abstract (arXiv)
In social dilemma games, additional rewards or punishments have been studied as means of promoting cooperation. Therefore, it is important to investigate the ideal situation, in which such an additional payoff would change the game. In this study, we investigated the symmetric solution of the Bellman optimality equation for a repeated harmony game. The calculations showed that three types of symmetric solutions exist. One of them corresponds to the trivial All-C strategy, and another to the Win-stay Lose-shift strategy of the prisoners dilemma game. The nontrivial behavior of the strategy corresponding to the last solution is also discussed in detail. In addition, we numerically investigated which strategy the agents actually learn by the reinforcement learning algorithm.
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References · 4
- 2017Learning with Opponent-Learning Awarenesscited by 222
- 2020Learning to Incentivize Other Learning Agentscited by 57
- 2021Symmetric equilibrium of multi-agent reinforcement learning in repeated prisoner's dilemmacited by 4
- 2015Strategical incoherence regulates cooperation in social dilemmas on multiplex networkscited by 1
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