Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Keepaway Soccer: A Machine Learning Testbed
RoboCup 2001: Robot Soccer World Cup V
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Empirical Studies in Action Selection with Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A common genetic encoding for both direct and indirect encodings of networks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Acquiring evolvability through adaptive representations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Efficient learning of neural networks with evolutionary algorithms
Proceedings of the 29th DAGM conference on Pattern recognition
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Evolving Neural Networks for Online Reinforcement Learning
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Fuzzy CMAC with automatic state partition for reinforcementlearning
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Evolving an autonomous agent for non-Markovian reinforcement learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Autonomous Agents and Multi-Agent Systems
CMA-TWEANN: efficient optimization of neural networks via self-adaptation and seamless augmentation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Many multiagent problems comprise subtasks which can be considered as reinforcement learning (RL) problems. In addition to classical temporal difference methods, evolutionary algorithms are among the most promising approaches for such RL problems. The relative performance of these approaches in certain subdomains (e. g. multiagent learning) of the general RL problem remains an open question at this time. In addition to theoretical analysis, benchmarks are one of the most important tools for comparing different RL methods in certain problem domains. A recently proposed multiagent RL benchmark problem is the RoboCup Keepaway benchmark. This benchmark is one of the most challenging multiagent learning problems because its state-space is continuous and high dimensional, and both the sensors and the actuators are noisy. In this paper we analyze the performance of the neuroevolutionary approach called Evolutionary Acquisition of Neural Topologies (EANT) in the Keepaway benchmark, and compare the results obtained using EANT with the results of other algorithms tested on the same benchmark.