Reinforcement Learning

Reinforcement Learning

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Overview

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 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 across a broad range of applications, including dialogue systems, game playing, content placement, program synthesis, recommendations, web search, natural language processing, and systems optimization.

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Provably efficient reinforcement learning with rich observations

Reinforcement learning, a machine learning paradigm for sequential decision making, has stormed into the limelight, receiving tremendous attention from both researchers and practitioners. When combined with deep learning, reinforcement learning (RL)…

Microsoft Research Blog | June 3, 2019

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