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Full-Text Articles in Physical Sciences and Mathematics
Variable Resolution Discretization In The Joint Space, Christopher K. Monson, Kevin Seppi, David Wingate, Todd S. Peterson
Variable Resolution Discretization In The Joint Space, Christopher K. Monson, Kevin Seppi, David Wingate, Todd S. Peterson
Faculty Publications
We present JoSTLe, an algorithm that performs value iteration on control problems with continuous actions, allowing this useful reinforcement learning technique to be applied to problems where a priori action discretization is inadequate. The algorithm is an extension of a variable resolution technique that works for problems with continuous states and discrete actions. Results are given that indicate that JoSTLe is a promising step toward reinforcement learning in a fully continuous domain.
Incremental Policy Learning: An Equilibrium Selection Algorithm For Reinforcement Learning Agents With Common Interests, Nancy Fulda, Dan A. Ventura
Incremental Policy Learning: An Equilibrium Selection Algorithm For Reinforcement Learning Agents With Common Interests, Nancy Fulda, Dan A. Ventura
Faculty Publications
We present an equilibrium selection algorithm for reinforcement learning agents that incrementally adjusts the probability of executing each action based on the desirability of the outcome obtained in the last time step. The algorithm assumes that at least one coordination equilibrium exists and requires that the agents have a heuristic for determining whether or not the equilibrium was obtained. In deterministic environments with one or more strict coordination equilibria, the algorithm will learn to play an optimal equilibrium as long as the heuristic is accurate. Empirical data demonstrate that the algorithm is also effective in stochastic environments and is able …
Solving Large Mdps Quickly With Partitioned Value Iteration, David Wingate
Solving Large Mdps Quickly With Partitioned Value Iteration, David Wingate
Theses and Dissertations
Value iteration is not typically considered a viable algorithm for solving large-scale MDPs because it converges too slowly. However, its performance can be dramatically improved by eliminating redundant or useless backups, and by backing up states in the right order. We present several methods designed to help structure value dependency, and present a systematic study of companion prioritization techniques which focus computation in useful regions of the state space. In order to scale to solve ever larger problems, we evaluate all enhancements and methods in the context of parallelizability. Using the enhancements, we discover that in many instances the limiting …