Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks
This work addresses cooperation problems in multi-agent reinforcement learning for social organisms, presenting an incremental improvement over existing methods.
The paper tackles the challenge of achieving cooperation in multi-agent systems where individual rationality leads to collective irrationality, proposing a learning strategy that balances exploration and exploitation to improve collective returns in intertemporal social dilemma tasks, with agents in heterogeneous populations showing better coordination.
When an individual's behavior has rational characteristics, this may lead to irrational collective actions for the group. A wide range of organisms from animals to humans often evolve the social attribute of cooperation to meet this challenge. Therefore, cooperation among individuals is of great significance for allowing social organisms to adapt to changes in the natural environment. Based on multi-agent reinforcement learning, we propose a new learning strategy for achieving coordination by incorporating a learning rate that can balance exploration and exploitation. We demonstrate that agents that use the simple strategy improve a relatively collective return in a decision task called the intertemporal social dilemma, where the conflict between the individual and the group is particularly sharp. We also explore the effects of the diversity of learning rates on the population of reinforcement learning agents and show that agents trained in heterogeneous populations develop particularly coordinated policies relative to those trained in homogeneous populations.