ML Day 2014 – Learning to Act in Multiagent Sequential Environments

From routing to online auctions, many decision-making tasks for learning agents are carried out in the presence of other decision makers. I will give a brief overview of results developed in the context of adapting reinforcement-learning algorithms to work effectively in multiagent environments. Of particular interest is the idea that even simple scenarios, such as the well-known Prisoner’s dilemma, require agents to work together, bearing some individual risk, to arrive at mutually beneficial outcomes