Concurrent reinforcement learning as a rehearsal for decentralized planning under uncertainty

12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013

Volumn 2, Issue , 2013, Pages 1291-1292

  Kraemer, Landon ^a   Banerjee, Bikramjit ^a  

^a UNIVERSITY OF SOUTHERN MISSISSIPPI   (United States)

Author keywords

Decentralized partially observable Markov decision processes; Multi agent reinforcement learning

Indexed keywords

AUTONOMOUS AGENTS; BENCHMARKING; MARKOV PROCESSES; MULTI AGENT SYSTEMS; REINFORCEMENT LEARNING;

CENTRALIZED COMPUTATION; DECENTRALIZED PLANNING; DISTRIBUTED SOLUTIONS; MARKOV DECISION PROCESSES; MULTI-AGENT PLANNING; MULTI-AGENT REINFORCEMENT LEARNING; PARTIALLY OBSERVABLE MARKOV DECISION PROCESS; SOLUTION TECHNIQUES;

UNCERTAINTY ANALYSIS;

EID: 84899441707     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. B. Banerjee, J. Lyle, L. Kraemer, and R. Yellamraju. Sample bounded distributed reinforcement learning for decentralized pomdps. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pages 1256-1262, Toronto, Canada, July 2012.
2. D. S. Bernstein, R. Givan, N. Immerman, and S. Zilberstein. The complexity of decentralized control of markov decision processes. Mathematics of Operations Research, 27:819-840, 2002.
3. M. T. J. Spaan, F. A. Oliehoek, and C. Amato. Scaling up optimal heuristic search in Dec-POMDPs via incremental expansion. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI-11), pages 2027-2032, Barcelona, Spain, 2011.
4. C. Zhang and V. Lesser. Coordinated multi-agent reinforcement learning in networked distributed POMDPs. In Proc. AAAI-11, San Francisco, CA, 2011.