Reinforcement learning is the study of decision making over time with consequences. The field has developed systems to make decisions in complex environments based on external, and possibly delayed, feedback.

At Microsoft Research, we are working on building the reinforcement learning theory, algorithms and systems for technology that learns from its own successes (and failures), explores the world “just enough” to learn, and can infer which decisions have led to those outcomes. Our primary goal is reinforcement learning in the real world: understanding how to build systems that work, even when simulation is unavailable, and samples are scarce.

We are working to create the future of reinforcement learning across a broad range of applications, including dialogue systems, game playing, content placement, program synthesis, recommendations, web search, natural language processing, and systems optimization.