-
1
-
-
33646244605
-
Survey of approximate methods for solving partially observable markov decision processes
-
(revised), Australia
-
D. Aberdeen. (revised) survey of approximate methods for solving partially observable Markov decision processes. Technical report, National ICT Australia, Canberra, Australia, 2003.
-
(2003)
Technical Report, National ICT Australia, Canberra
-
-
Aberdeen, D.1
-
2
-
-
0036874366
-
The complexity of decentralized control of markov decision processes
-
D. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov decision processes. Mathematics of Operations Research, 27(4):819-840, 2002.
-
(2002)
Mathematics of Operations Research
, vol.27
, Issue.4
, pp. 819-840
-
-
Bernstein, D.1
Zilberstein, S.2
Immerman, N.3
-
5
-
-
0031630561
-
The dynamics of reinforcement learning in cooperative multiagent systems
-
C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proc. Nat. Conf. Artificial Intelligence, pages 746-752, 1998.
-
(1998)
Proc. Nat. Conf. Artificial Intelligence
, pp. 746-752
-
-
Claus, C.1
Boutilier, C.2
-
6
-
-
33846942607
-
Hierarchical multi-agent reinforcement learning
-
M. Ghavamzadeh, S. Mahadevan, and R. Makar. Hierarchical multi-agent reinforcement learning. J. Autonomous Agents and Multi-Agent Systems, 13(2): 197-229, 2006.
-
(2006)
J. Autonomous Agents and Multi-Agent Systems
, vol.13
, Issue.2
, pp. 197-229
-
-
Ghavamzadeh, M.1
Mahadevan, S.2
Makar, R.3
-
10
-
-
40949099898
-
Utile coordination: Learning interdependencies among cooperative agents
-
J. Kok, P. Hoen, B. Bakker, and N. Vlassis. Utile coordination: Learning interdependencies among cooperative agents. In Proc. Symp. on Computational Intelligence and Games, pages 29-36, 2005.
-
(2005)
Proc. Symp. on Computational Intelligence and Games
, pp. 29-36
-
-
Kok, J.1
Hoen, P.2
Bakker, B.3
Vlassis, N.4
-
13
-
-
0001547175
-
Value-function reinforcement learning in markov games
-
M. Littman. Value-function reinforcement learning in Markov games. J. Cognitive Systems Research, 2(1):55-66, 2001.
-
(2001)
J. Cognitive Systems Research
, vol.2
, Issue.1
, pp. 55-66
-
-
Littman, M.1
-
15
-
-
60349107649
-
Exploiting factored representations for decentralized execution in multi-agent teams
-
M. Roth, R. Simmons, and M. Veloso. Exploiting factored representations for decentralized execution in multi-agent teams. In Proc. AAMAS, pages 469-75, 2007.
-
(2007)
Proc. AAMAS
, pp. 469-475
-
-
Roth, M.1
Simmons, R.2
Veloso, M.3
-
17
-
-
0038637209
-
Multi-agent reinforcement learning: Independent vs. Cooperative agents
-
M. Tan. Multi-agent reinforcement learning: Independent vs. cooperative agents. In Readings in Agents, pages 487-194, 1997.
-
(1997)
Readings in Agents
, pp. 194-487
-
-
Tan, M.1
-
18
-
-
67649405225
-
Reinforcement learning to play an optimal nash equilibrium in team markov games
-
X. Wang and T. Sandholm. Reinforcement learning to play an optimal Nash equilibrium in team Markov games. In Proc. NIPS 15, pages 1571-1578, 2002.
-
(2002)
Proc. NIPS
, vol.15
, pp. 1571-1578
-
-
Wang, X.1
Sandholm, T.2
|