The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
${{\cal Q} {\cal D}}$-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through ${\rm Consensus} + {\rm Innovations}$
Open accessAbstract
The paper developsQ D-learning, a distributed version of reinforcementQ-learning, for multi-agent Markov decision processes (MDPs); the agents have no prior information on the global state transition and on the local agent cost statistics. The network agents minimize a network-averaged infinite horizon discounted cost, by local processing and by collaborating through mutual information exchange over a sparse (possibly stochastic) communication network. The agents respond differently (depending on their instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. When each agent is aware only of its local online cost data and the inter-agent communication network is weakly connected, we prove thatQ D-learning, a consensus + innovations algorithm with mixed time-scale stochastic dynamics, converges asymptotically almost surely to the desired value function and to the optimal stationary control policy at each network agent.
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Citations
Cites
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788 · cited 1×
“As such, the resulting value function update processes at the agents are mixed time-scale, in which the distinct potentials of consensus (corresponding to information mixing through neighborhood communication [30], [31], [32], [29], [33], [34], [35]) and local innovation (corresponding to the instantaneous locally sen…”
BackgroundConsensus and Cooperation in Networked Multi-Agent Systems
Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2006.887293 · Cited by 10,522 · cited 1×
“As such, the resulting value function update processes at the agents are mixed time-scale, in which the distinct potentials of consensus (corresponding to information mixing through neighborhood communication [30], [31], [32], [29], [33], [34], [35]) and local innovation (corresponding to the instantaneous locally sen…”
BackgroundWatkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Coordination of groups of mobile autonomous agents using nearest neighbor rules
Jadbabaie, A., Lin, J., & Morse, A. S. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.812781 · Cited by 8,339
Distributed Subgradient Methods for Multi-Agent Optimization
Nedic, A., & Ozdaglar, A. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.2009515 · Cited by 3,719
A Comprehensive Survey of Multiagent Reinforcement Learning
Busoniu, L., Babuska, R., & De Schutter, B. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.913919 · Cited by 2,235
Distributed asynchronous deterministic and stochastic gradient optimization algorithms
Tsitsiklis, J., Bertsekas, D., & Athans, M. (1986) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1986.1104412 · Cited by 1,967
On Distributed Averaging Algorithms and Quantization Effects
Nedic, A., Olshevsky, A., Ozdaglar, A., & Tsitsiklis, J. N. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2031203 · Cited by 557
Cited by
A review of cooperative multi-agent deep reinforcement learning
Oroojlooy, A., & Hajinezhad, D. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04105-y · Cited by 482
Applications of reinforcement learning in energy systems
Perera, A. T. D., & Kamalaruban, P. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110618 · Cited by 420
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Zhang, K., Yang, Z., Liu, H., Zhang, T., & Başar, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08757 · Cited by 293
Optimization for Reinforcement Learning: From a single agent to cooperative agents
Lee, D., He, N., Kamalaruban, P., & Cevher, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2976000 · Cited by 113
Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning
Chen, T., Zhang, K., Giannakis, G. B., & Basar, T. (2022) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2021.3078100 · Cited by 67
Metadata
- Authors
- Kar, S., Moura, J. M. F., & Poor, H. V.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2013-04
- Volume
- 61
- Issue
- 7
- Pages
- 1848-1862
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W1918371733
- Cited by
- 175
- References
- 8
- FWCI
- 6.61
- Type
- journal article
- Language
- en
- Open access
- green
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- Kar, S., Moura, J. M. F., & Poor, H. V. (2013). ${{\cal Q} {\cal D}}$-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through ${\rm Consensus} + {\rm Innovations}$. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2013.2241057
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
