Name
Abstract | ||
|---|---|---|
Psychologists call behavior intrinsically motivated when it is engaged in for its own sake rather than as a step toward solving a specific problem of clear practical value. But what we learn during intrinsically motivated behavior is essential for our development as competent autonomous en- tities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated reinforcement learningaimed at allowing arti- ficial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. |
Year | Venue | Keywords |
|---|---|---|
2004 | NIPS | intrinsic motivation,computations,reinforcement learning,behavior |
Field | DocType | Citations |
Computer science,Autonomy,Artificial intelligence,Hierarchy,Machine learning,Reinforcement learning | Conference | 142 |
PageRank | References | Authors |
8.20 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Satinder P. Singh | 1 | 5508 | 715.52 |
| Andrew G. Barto | 2 | 3937 | 829.22 |
| Nuttapong Chentanez | 3 | 675 | 38.02 |