Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Predictive state representations: a new theory for modeling dynamical systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
TD(λ) networks: temporal-difference networks with eligibility traces
ICML '05 Proceedings of the 22nd international conference on Machine learning
Analyzing feature generation for value-function approximation
Proceedings of the 24th international conference on Machine learning
On-line discovery of temporal-difference networks
Proceedings of the 25th international conference on Machine learning
Approximate predictive state representations
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Exponential family predictive representations of state
Exponential family predictive representations of state
Representation Discovery using Harmonic Analysis
Representation Discovery using Harmonic Analysis
Using predictive representations to improve generalization in reinforcement learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Temporal-difference (TD) networks are a class of predictive state representations that use well-established TD methods to learn models of partially observable dynamical systems. Previous research with TD networks has dealt only with dynamical systems with finite sets of observations and actions. We present an algorithm for learning TD network representations of dynamical systems with continuous observations and actions. Our results show that the algorithm is capable of learning accurate and robust models of several noisy continuous dynamical systems. The algorithm presented here is the first fully incremental method for learning a predictive representation of a continuous dynamical system.