A stochastic game is defined as a tuple (n, S, A 1...n , T , R 1...n , γ) where n is the number of agents, S is the set of states, A i is the set of actions available for agent i (and A is the joint ac-tion space A 1 × A 2 × ... × A n ), T is the transition function S × A × S → [0, 1] which defines a set of transition probabilities between states after taking a joint action, R i is the reward function for the ith agent S × A → , and γ is a factor that discounts future rewards.
3 Single-Agent vs. Multi-Agent Reinforcement Learning Reinforcement Learning (RL) is a machine learning technique used to learn the policy of an agent, i.e., which action the agent should perform given its current state (Sutton and Barto, 1998).