Learning to Perceive and Act by Trial and Error
Machine Learning
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Multiagent Coordination with Learning Classifier Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Behavior transfer for value-function-based reinforcement learning
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Two steps reinforcement learning
International Journal of Intelligent Systems
New Insights into Pedestrian Flow Through Bottlenecks
Transportation Science
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Probabilistic Policy Reuse for inter-task transfer learning
Robotics and Autonomous Systems
Pengi: an implementation of a theory of activity
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
IEEE Transactions on Neural Networks
Levels of realism for cooperative multi-agent reinforcement learning
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
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In this paper we introduce a Multi-agent system that uses Reinforcement Learning (RL) techniques to learn local navigational behaviors to simulate virtual pedestrian groups. The aim of the paper is to study empirically the validity of RL to learn agent-based navigation controllers and their transfer capabilities when they are used in simulation environments with a higher number of agents than in the learned scenario. Two RL algorithms which use Vector Quantization (VQ) as the generalization method for the space state are presented. Both strategies are focused on obtaining a good vector quantizier that generalizes adequately the state space of the agents. We empirically state the convergence of both methods in our navigational Multi-agent learning domain. Besides, we use validation tools of pedestrian models to analyze the simulation results in the context of pedestrian dynamics. The simulations carried out, scaling up the number of agents in our environment (a closed room with a door through which the agents have to leave), have revealed that the basic characteristics of pedestrian movements have been learned.