The best way to do this rapidly is by using a simulation environment. A major reason is that Deep RL often requires an agent to experiment millions of times before learning anything useful. However, industry applications have trailed behind the rapidly advancing results coming out of the research community.
This makes Deep RL particularly attractive for tasks that require planning and adaptation, such as manufacturing or self-driving. On the other hand, Deep Reinforcement Learning focuses on the right sequences of sentences that will lead to a positive outcome, for example a happy customer. In dialog systems for example, classical Deep Learning aims to learn the right response for a given query. Contrary to many classical Deep Learning problems that often focus on perception (does this image contain a stop sign?), Deep RL adds the dimension of actions that influence the environment (what is the goal, and how do I get there?).
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If anything is unclear, reach out to me here! The rise of Deep Reinforcement Learningĭeep RL is a field that has seen vast amounts of research interest, including learning to play Atari games, beating pro players at Dota 2, and defeating Go champions. Many of the visuals are from the slides of the talk, and some are new. In this post, I will give an overview of core aspects of the field that can be understood by anyone. In parallel, the inner workings and applications of Deep RL, such as AlphaGo pictured above, can often seem esoteric and hard to understand. In our conversations with companies, we’ve seen a rise of interesting Deep RL applications, tools and results.
I thought that the session, led by Arthur Juliani, was extremely informative and wanted to share some big takeaways below. While there, I was lucky enough to attend a tutorial on Deep Reinforcement Learning (Deep RL) from scratch by Unity Technologies. Recently, I gave a talk at the O’Reilly AI conference in Beijing about some of the interesting lessons we’ve learned in the world of NLP.
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