Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Artificial intelligence and mobile robots
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Methods for Competitive Co-Evolution: Finding Opponents Worth Beating
Proceedings of the 6th International Conference on Genetic Algorithms
Learning In RoboCup Keepaway Using Evolutionary Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic Programming And Multi-agent Layered Learning By Reinforcements
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolving Beharioral Strategies in Predators and Prey
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Keepaway Soccer: A Machine Learning Testbed
RoboCup 2001: Robot Soccer World Cup V
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Evolving keepaway soccer players through task decomposition
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An Architecture for Behavior-Based Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Evolving Soccer Keepaway Players Through Task Decomposition
Machine Learning
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Simulation and reinforcement learning with soccer agents
Multiagent and Grid Systems - Innovations in intelligent agent technology
A role-oriented BDI framework for real-time multiagent teaming
Intelligent Decision Technologies
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Evolving keepaway soccer players through task decomposition
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
ML-CIDIM: multiple layers of multiple classifier systems based on CIDIM
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
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Hierarchies are powerful tools for decomposing complex control tasks into manageable subtasks. Several hierarchical approaches have been proposed for creating agents that can execute these tasks. Layered learning is such a hierarchical paradigm that relies on learning the various subtasks necessary for achieving the complete high-level goal. Layered learning prescribes training low-level behaviors (those closer to the environmental inputs) prior to high-level behaviors. In past implementations these lower-level behaviors were always frozen before advancing to the next layer. In this paper, we hypothesize that there are situations where layered learning would work better were the lower layers allowed to keep learning concurrently with the training of subsequent layers, an approach we call concurrent layered learning. We identify a situation where concurrent layered learning is beneficial and present detailed empirical results verifying our hypothesis. In particular, we use neuro-evolution to concurrently learn two layers of a layered learning approach to a simulated robotic soccer keepaway task. The main contribution of this paper is evidence that there exist situations where concurrent layered learning outperforms traditional layered learning. Thus, we establish that, when using layered learning, the concurrent training of layers can be an effective option.