Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Technical Note: \cal Q-Learning
Machine Learning
Reinforcement Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Reinforcement learning scheme for grouping and anti-predator behavior
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Evaluating Q-learning policies for multi-objective foraging task in a multi-agent environment
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
Exploration strategies for learning in multi-agent foraging
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
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Grouping motion, such as bird flocking, land animal herding, and fish schooling, is well-known in nature. Many observations have shown that there are no leading agents to control the behavior of the group. Several models have been proposed for describing the flocking behavior, which we regard as a distinctive example of the aggregate motions. In these models, some fixed rule is given to each of the individuals a priori for their interactions in reductive and rigid manner. Instead of this, we have proposed a new framework for self-organized flocking of agents by reinforcement learning. It will become important to introduce a learning scheme for making collective behavior in artificial autonomous distributed systems. In this paper, anti-predator behaviors of agents are examined by our scheme through computer simulations. We demonstrate the feature of behavior under two learning modes against agents of the same kind and predators.