Practical Issues in Temporal Difference Learning
Machine Learning
Machine Learning - Special issue on inductive transfer
The spatial semantic hierarchy
Artificial Intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Reinforcement Learning for 3 vs. 2 Keepaway
RoboCup 2000: Robot Soccer World Cup IV
Learning One More Thing
Evolving Soccer Keepaway Players Through Task Decomposition
Machine Learning
Improving reinforcement learning function approximators via neuroevolution
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
The Journal of Machine Learning Research
Transfer via inter-task mappings in policy search reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Performance Evaluation of EANT in the RoboCup Keepaway Benchmark
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
An object-oriented representation for efficient reinforcement learning
Proceedings of the 25th international conference on Machine learning
Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study
RoboCup 2006: Robot Soccer World Cup X
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling
ECML '07 Proceedings of the 18th European conference on Machine Learning
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Stochastic optimization for collision selection in high energy physics
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
A case study on the critical role of geometric regularity in machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
An experts algorithm for transfer learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On-line neuroevolution applied to the open racing car simulator
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on 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
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Constraining connectivity to encourage modularity in HyperNEAT
Proceedings of the 13th annual conference 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
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
Critical factors in the performance of hyperNEAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Encouraging reactivity to create robust machines
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Previous approaches to transfer in Keepaway have focused on transforming the original representation to fit the new task. In contrast, this paper explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To demonstrate this point, a bird's eye view (BEV) representation is introduced that can represent different tasks on the same two-dimensional map. For example, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV. Yet the problem is that a raw two-dimensional map is high-dimensional and unstructured. This paper shows how this problem is addressed naturally by an idea from evolutionary computation called indirect encoding, which compresses the representation by exploiting its geometry. The result is that the BEV learns a Keepaway policy that transfers without further learning or manipulation. It also facilitates transferring knowledge learned in a different domain, Knight Joust, into Keepaway. Finally, the indirect encoding of the BEV means that its geometry can be changed without altering the solution. Thus static representations facilitate several kinds of transfer.