| 1 |
Deep Teams: Decentralized Decision Making With Finite and Infinite Number of Agents
Arabneydi J, Aghdam AG
IEEE Transactions on Automatic Control, 65(10), 4230, 2020 |
| 2 |
Controlled Markov Processes With Safety State Constraints
El Chamie M, Yu Y, Acikmese B, Ono M
IEEE Transactions on Automatic Control, 64(3), 1003, 2019 |
| 3 |
Ergodic control of continuous-time Markov chains with pathwise constraints
Prieto-Rumeau T, Hernandez-Lerma O
SIAM Journal on Control and Optimization, 47(4), 1888, 2008 |
| 4 |
Dynamic programming for ergodic control of Markov chains under partial observations (vol 39, pg 673, 2000)
Borkar VS
SIAM Journal on Control and Optimization, 45(6), 2299, 2007 |
| 5 |
Drift and monotonicity conditions for continuous-time controlled Markov chains with an average criterion
Guo XP, Hernandez-Lerma O
IEEE Transactions on Automatic Control, 48(2), 236, 2003 |
| 6 |
Adaptive policy for two finite Markov chains zero-sum stochastic game with unknown transition matrices and average payoffs
Najim K, Poznyak AS, Gomez E
Automatica, 37(7), 1007, 2001 |
| 7 |
Learning algorithms or Markov decision processes with average cost
Abounadi J, Bertsekas D, Borkar VS
SIAM Journal on Control and Optimization, 40(3), 681, 2001 |
| 8 |
Average cost dynamic programming equations for controlled Markov chains with partial observations
Borkar VS
SIAM Journal on Control and Optimization, 39(3), 673, 2000 |
