Name
Abstract | ||
|---|---|---|
Policy Gradients with Parameter-based Exploration (PGPE) is a novel model-free reinforcement learning method that alleviates the problem of high-variance gradient estimates encountered in normal policy gradient methods. It has been shown to drastically speed up convergence for several large-scale reinforcement learning tasks. However the independent normal distributions used by PGPE to search through parameter space are inadequate for some problems with multimodal reward surfaces. This paper extends the basic PGPE algorithm to use multimodal mixture distributions for each parameter, while remaining efficient. Experimental results on the Rastrigin function and the inverted pendulum benchmark demonstrate the advantages of this modification, with faster convergence to better optima. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICMLA.2010.24 | ICMLA |
Keywords | Field | DocType |
multimodal parameter-exploring policy gradients,normal policy gradient method,faster convergence,basic pgpe algorithm,independent normal distribution,high-variance gradient,multimodal mixture distribution,large-scale reinforcement,novel model-free reinforcement,parameter space,multimodal reward surface,inverted pendulum,gradient method,reinforcement learning,learning artificial intelligence,normal distribution,mixture distribution | Convergence (routing),Mathematical optimization,Normal distribution,Inverted pendulum,Computer science,Rastrigin function,Parameter space,Artificial intelligence,Machine learning,Reinforcement learning,Speedup | Conference |
Citations | PageRank | References |
4 | 0.45 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Sehnke | 1 | 527 | 39.18 |
| Graves, Alex | 2 | 8572 | 405.10 |
| Christian Osendorfer | 3 | 125 | 13.24 |
| Jürgen Schmidhuber | 4 | 17836 | 1238.63 |