Name
Abstract | ||
|---|---|---|
We study the problem of learning stochastic actions in propositional, factored environments, and precisely the problem of identifying STRIPS-like effects from transitions in which they are ambiguous. We give an unbiased, maximum likelihood approach, and show that maximally likely actions can be computed efficiently from observations. We also discuss how this study can be used to extend an RL approach for actions with independent effects to one for actions with correlated effects. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-29946-9_9 | EWRL |
Keywords | Field | DocType |
maximally likely action,strips-like effect,ambiguous effect,action learning,correlated effect,rl approach,factored environment,maximum likelihood approach,stochastic action,independent effect,maximum likelihood | Computer science,Maximum likelihood,Action learning,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Lesner | 1 | 36 | 4.77 |
| Bruno Zanuttini | 2 | 289 | 25.43 |