Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Policy Iteration for Factored MDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Fast Monte-Carlo Algorithms for finding low-rank approximations
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-squares policy iteration
The Journal of Machine Learning Research
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning state-action basis functions for hierarchical MDPs
Proceedings of the 24th international conference on Machine learning
Manifold alignment using Procrustes analysis
Proceedings of the 25th international conference on Machine learning
Graph Laplacian based transfer learning in reinforcement learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Geodesic Gaussian kernels for value function approximation
Autonomous Robots
Reinforcement Learning in Nonstationary Environment Navigation Tasks
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Learning Representation and Control in Markov Decision Processes: New Frontiers
Foundations and Trends® in Machine Learning
Learning representation and control in continuous Markov decision processes
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Basis function construction for hierarchical reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Basis function discovery using spectral clustering and bisimulation metrics
ALA'11 Proceedings of the 11th international conference on Adaptive and Learning Agents
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathematical model of a proto-value function: these are task-independent basis functions that form the building blocks of all value functions on a given state space manifold. Proto-value functions are learned not from rewards, but instead from analyzing the topology of the state space. Formally, proto-value functions are Fourier eigenfunctions of the Laplace-Beltrami diffusion operator on the state space manifold. Proto-value functions facilitate structural decomposition of large state spaces, and form geodesically smooth orthonormal basis functions for approximating any value function. The theoretical basis for proto-value functions combines insights from spectral graph theory, harmonic analysis, and Riemannian manifolds. Proto-value functions enable a novel generation of algorithms called representation policy iteration, unifying the learning of representation and behavior.