The MDPs of both agents are explicitly defined above now, and we can use the typical REINFORCE algorithm (Williams, 1992) to train the two agents.
their goals are maximizing the reward expectation, defined as where R(s, a) is the reward of selecting a given s, and π θ (a|s) is the policy learned by the agents and will be defined formally in Section.