Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Adding Continuous Components to L-Systems
L Systems, Most of the papers were presented at a conference in Aarhus, Denmark
A Taxonomy for artificial embryogeny
Artificial Life
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
A novel generative encoding for exploiting neural network sensor and output geometry
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Efficient reinforcement learning with relocatable action models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Function approximation via tile coding: automating parameter choice
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Evolving an expert checkers playing program without using humanexpertise
IEEE Transactions on Evolutionary Computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Generative and developmental systems
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
The sensitivity of HyperNEAT to different geometric representations of a problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Generative and developmental systems
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Neuroevolution based on reusable and hierarchical modular representation
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Transfer learning through indirect encoding
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving the placement and density of neurons in the hyperneat substrate
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Investigating whether hyperNEAT produces modular neural networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Generative and developmental systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evolving policy geometry for scalable multiagent learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
Indirectly encoding neural plasticity as a pattern of local rules
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Indirect encoding of neural networks for scalable go
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Evolving neural networks for geometric game-tree pruning
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Constraining connectivity to encourage modularity in HyperNEAT
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Enhancing es-hyperneat to evolve more complex regular neural networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
HyperNEAT-GGP: a hyperNEAT-based atari general game player
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Generative and developmental systems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Confronting the challenge of learning a flexible neural controller for a diversity of morphologies
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Ribosomal robots: evolved designs inspired by protein folding
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Critical factors in the performance of hyperNEAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Generative and developmental systems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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An important feature of many problem domains in machine learning is their geometry. For example, adjacency relationships, symmetries, and Cartesian coordinates are essential to any complete description of board games, visual recognition, or vehicle control. Yet many approaches to learning ignore such information in their representations, instead inputting flat parameter vectors with no indication of how those parameters are situated geometrically. This paper argues that such geometric information is critical to the ability of any machine learning approach to effectively generalize; even a small shift in the configuration of the task in space from what was experienced in training can go wholly unrecognized unless the algorithm is able to learn the regularities in decision-making across the problem geometry. To demonstrate the importance of learning from geometry, three variants of the same evolutionary learning algorithm (NeuroEvolution of Augmenting Topologies), whose representations vary in their capacity to encode geometry, are compared in checkers. The result is that the variant that can learn geometric regularities produces a significantly more general solution. The conclusion is that it is important to enable machine learning to detect and thereby learn from the geometry of its problems.