Abstract: Decision making in dynamic uncertain environments with multiple agents arises in many disciplines and application domains, including control, communications, distributed optimization, social networks, and economics. Here a natural framework, and a comprehensive one, for modeling, optimization, and analysis is the one provided by stochastic dynamic games (SDGs), which accommodates different solution concepts depending on how the interactions among the agents are modeled, particularly whether they are in a cooperative mode (with the same objective functions, as in teams) or in a noncooperative mode (with different objective functions) or a mix of the two, such as teams of agents interacting noncooperatively across different teams (and of course cooperatively within each team). What also affects (strategic) interactions among the agents is the asymmetric nature of the information different agents acquire (and do not share or only partially share (selectively) with others, even within teams). What makes such problems even more challenging in a dynamic environment with networked agents is the dependence of the information available to one agent at some point in time on the policies or decisions of other agents who have already acted at earlier instants of time. Such decision problems, initially studied in a team framework, are known as those with nonclassical information where optimal policies of team agents must be designed to balance a tradeoff between contribution to optimality of the team objective function and signaling through their actions useful information to other agents in their neighborhood who would be acting after them. Existence of such a tradeoff between signaling and optimization creates even more challenging issues in SDGs with mis-aligned objectives among at least a subset of agents, which however can be addressed effectively for a specially structured subclass of such games, namely mean-field games. This talk will provide an overview of the landscape above, first for a general class of stochastic dynamic teams and games, and then for a subclass where the objective functions are quadratic, and the interaction relationships are linear. The talk will also cover reinforcement learning embedded into policy development when agents do not have precise information on the underlying models.