|
| << vorige | volgende >> |
Tijdschrift beschrijving
Alle jaargangen van het bijbehorende tijdschrift
Alle afleveringen van het bijbehorende jaargang
Alle artikelen van de bijbehorende aflevering
Details van artikel 3 van 4 gevonden artikelen
| Titel: |
On-policy concurrent reinforcement learning |
| Auteur: |
Banerjee, Bikramjit Sen, Sandip Peng, Jing |
| Verschenen in: |
Journal of experimental & theoretical artificial intelligence |
| Paginering: | Jaargang 16 (2004) nr. 4 pagina's 245-260 |
| Jaar: |
2004-10 |
| Inhoud: |
When an agent learns in a multi-agent environment, the payoff it receives is dependent on the behaviour of the other agents. If the other agents are also learning, its reward distribution becomes non-stationary. This makes learning in multi-agent systems more difficult than single-agent learning. Prior attempts at value-function based learning in such domains have used off-policy Q-learning that do not scale well as the cornerstone, with restricted success. This paper studies on-policy modifications of such algorithms, with the promise of scalability and efficiency. In particular, it is proven that these hybrid techniques are guaranteed to converge to their desired fixed points under some restrictions. It is also shown, experimentally, that the new techniques can learn (from self-play) better policies than the previous algorithms (also in self-play) during some phases of the exploration. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 3 van 4 gevonden artikelen
| << vorige | volgende >> |