Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Reinforcement learning in Markovian and non-Markovian environments
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Back propagation is sensitive to initial conditions
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Natural gradient works efficiently in learning
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Evolving neural networks through augmenting topologies
Evolutionary Computation
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Neural Computation
ECML '07 Proceedings of the 18th European conference on Machine Learning
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
High dimensions and heavy tails for natural evolution strategies
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Unsupervised modeling of partially observable environments
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Modular value iteration through regional decomposition
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
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Traditional Reinforcement Learning methods are insufficient for AGIs who must be able to learn to deal with Partially Observable Markov Decision Processes. We investigate a novel method for dealing with this problem: standard RL techniques using as input the hidden layer output of a Sequential Constant-Size Compressor (SCSC). The SCSC takes the form of a sequential Recurrent Auto-Associative Memory, trained through standard back-propagation. Results illustrate the feasibility of this approach -- this system learns to deal with highdimensional visual observations (up to 640 pixels) in partially observable environments where there are long time lags (up to 12 steps) between relevant sensory information and necessary action.