Name
Abstract | ||
|---|---|---|
We discuss a variant of Thompson sampling for nonparametric reinforcement learning in countable classes of general stochastic environments. These environments can be non-Markov, non-ergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its value converges to the optimal value in mean and (2) given a recoverability assumption regret is sublinear. |
Year | Venue | DocType |
|---|---|---|
2016 | UAI | Conference |
Volume | ISBN | Citations |
abs/1602.07905 | 978-0-9966431-1-5 | 8 |
PageRank | References | Authors |
0.61 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Leike | 1 | 150 | 15.49 |
| Tor Lattimore | 2 | 174 | 29.15 |
| Laurent Orseau | 3 | 144 | 18.23 |
| Marcus Hutter | 4 | 1302 | 132.09 |