Nash-reinforcement learning (N-RL) for developing coordination strategies in non-transferable utility games.
Kaveh MadaniMilad HooshyarSina KhatamiAli AlaeipourAida MoeiniPublished in: SMC (2014)
Keyphrases
- reinforcement learning
- solution concepts
- nash equilibrium
- transferable utility
- nash equilibria
- multi agent reinforcement learning
- learning agents
- game theory
- stochastic games
- game theoretic
- multi agent
- rl algorithms
- coalitional games
- equilibrium strategies
- coalition formation
- multiagent learning
- cooperative game theory
- function approximation
- cooperative games
- model free
- cooperative
- state space
- multi agent systems
- learning algorithm
- multiagent systems
- incomplete information
- markov decision processes
- reinforcement learning algorithms
- learning agent
- action selection
- pareto optimal
- reinforcement learning methods
- von neumann
- continuous state
- autonomous agents
- utility function
- exploration strategy
- resource allocation
- optimal policy
- function approximators
- policy search
- single agent
- machine learning