Abstract
Partially observable stochastic games (POSGs) are an attractive model for many multi-agent domains, but are computationally extremely difficult to solve. We present a new model, Markov games of incomplete information (MGII) which imposes a mild restriction on POSGs while overcoming their primary computational bottleneck. Finally we show how to convert a MGII into a continuous but bounded fully observable stochastic game. MGIIs represents the most general tractable model for multi-agent reinforcement learning to date.
| Original language | English (US) |
|---|---|
| Title of host publication | Interactive Decision Theory and Game Theory - Papers from the 2011 AAAI Workshop, Technical Report |
| Pages | 43-51 |
| Number of pages | 9 |
| Publication status | Published - Nov 2 2011 |
| Event | 2011 AAAI Workshop - San Francisco, CA, United States Duration: Aug 8 2011 → Aug 8 2011 |
Publication series
| Name | AAAI Workshop - Technical Report |
|---|---|
| Volume | WS-11-13 |
Conference
| Conference | 2011 AAAI Workshop |
|---|---|
| Country | United States |
| City | San Francisco, CA |
| Period | 8/8/11 → 8/8/11 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
Mac Dermed, L., Isbell, C. L., & Weiss, L. G. (2011). Markov games of incomplete information for multi-agent reinforcement learning. In Interactive Decision Theory and Game Theory - Papers from the 2011 AAAI Workshop, Technical Report (pp. 43-51). (AAAI Workshop - Technical Report; Vol. WS-11-13).