Name
Abstract | ||
|---|---|---|
This paper proposes an extension of Reinforcement Learning (RL) to acquire co-operation among agents. The idea is to learn filtered payoff that reflects a global objective function but does not require mass communication among agents. It is shown that the acquisition of two typical co-operation tasks is realised by preparing simple filter functions: an averaging filter for co-operative tasks and an enhancement filter for deadlock prevention tasks. The performance of these systems was tested through computer simulations of n-persons prisoner's dilemma, and a traffic control problem. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1007/3-540-59286-5_77 | ECML |
Keywords | Field | DocType |
extended abstract,payoff filters,co-operative reinforcement learning,prisoner s dilemma,objective function,reinforcement learning,computer simulation | Computer science,Artificial intelligence,Dilemma,Deadlock prevention algorithms,Machine learning,Stochastic game,Reinforcement learning | Conference |
ISBN | Citations | PageRank |
3-540-59286-5 | 3 | 0.69 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sadayoshi Mikami | 1 | 41 | 16.40 |
| Yukinori Kakazu | 2 | 199 | 51.23 |
| T C Fogarty | 3 | 1147 | 152.53 |