Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Technical Note: \cal Q-Learning
Machine Learning
Associative Reinforcement Learning: Functions in k-DNF
Machine Learning
Robust and optimal control
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Adaptive critic designs: a case study for neurocontrol
Neural Networks
Competitive Markov decision processes
Competitive Markov decision processes
Multiagent learning using a variable learning rate
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Convergence Problems of General-Sum Multiagent Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Finite-memory control of partially observable systems
Finite-memory control of partially observable systems
A reinforcement learning adaptive fuzzy controller for robots
Fuzzy Sets and Systems - Theme: Modeling and control
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Evolutionary game theory and multi-agent reinforcement learning
The Knowledge Engineering Review
Neural Computation
Reinforcement Learning in Continuous Time and Space
Neural Computation
A robust Markov game controller for nonlinear systems
Applied Soft Computing
On the convergence of stochastic iterative dynamic programming algorithms
Neural Computation
Formal models and algorithms for decentralized decision making under uncertainty
Autonomous Agents and Multi-Agent Systems
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Optimal and approximate Q-value functions for decentralized POMDPs
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Planning for weakly-coupled partially observable stochastic games
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Value function approximation in zero-sum markov games
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive Critic Designs for Discrete-Time Zero-Sum Games With Application to Control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
A Markov Game-Adaptive Fuzzy Controller for Robot Manipulators
IEEE Transactions on Fuzzy Systems
Hybrid Game Strategy in Fuzzy Markov-Game-Based Control
IEEE Transactions on Fuzzy Systems
Value-function reinforcement learning in Markov games
Cognitive Systems Research
IEEE Transactions on Neural Networks
SVM-Based Tree-Type Neural Networks as a Critic in Adaptive Critic Designs for Control
IEEE Transactions on Neural Networks
Radial basis function neural network-based adaptive critic control of induction motors
Applied Soft Computing
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Reinforcement learning (RL) has now evolved as a major technique for adaptive optimal control of nonlinear systems. However, majority of the RL algorithms proposed so far impose a strong constraint on the structure of environment dynamics by assuming that it operates as a Markov decision process (MDP). An MDP framework envisages a single agent operating in a stationary environment thereby limiting the scope of application of RL to control problems. Recently, a new direction of research has focused on proposing Markov games as an alternative system model to enhance the generality and robustness of the RL based approaches. This paper aims to present this new direction that seeks to synergize broad areas of RL and Game theory, as an interesting and challenging avenue for designing intelligent and reliable controllers. First, we briefly review some representative RL algorithms for the sake of completeness and then describe the recent direction that seeks to integrate RL and game theory. Finally, open issues are identified and future research directions outlined.