Name
Title | ||
|---|---|---|
Reward function and initial values: better choices for accelerated goal-directed reinforcement learning |
Abstract | ||
|---|---|---|
An important issue in Reinforcement Learning (RL) is to accelerate or improve the learning process. In this paper, we study the influence of some RL parameters over the learning speed. Indeed, although RL convergence properties have been widely studied, no precise rules exist to correctly choose the reward function and initial Q-values. Our method helps the choice of these RL parameters within the context of reaching a goal in a minimal time. We develop a theoretical study and also provide experimental justifications for choosing on the one hand the reward function, and on the other hand particular initial Q-values based on a goal bias function. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11840817_87 | ICANN (1) |
Keywords | Field | DocType |
accelerated goal-directed reinforcement learning,goal bias function,experimental justification,important issue,hand particular initial q-values,initial q-values,initial value,rl parameter,rl convergence property,better choice,reinforcement learning,reward function,theoretical study | Convergence (routing),Computer science,Bellman equation,Initial value problem,Artificial intelligence,Estimation theory,Artificial neural network,Machine learning,Reinforcement learning | Conference |
Volume | ISSN | ISBN |
4131 | 0302-9743 | 3-540-38625-4 |
Citations | PageRank | References |
11 | 0.79 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laëtitia Matignon | 1 | 88 | 9.43 |
| Guillaume J. Laurent | 2 | 97 | 12.60 |
| Nadine Le Fort-Piat | 3 | 77 | 10.09 |