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April 10-11, 2018: Training
April 11-13, 2018: Tutorials & Conference
Beijing, CN

Schedule: 增强学习(Reinforcement Learning) sessions

One of the areas we’re most interested in is the emerging applications of reinforcement learning (RL). We’ve all read about the key role RL played in systems that learned how to exceed human players in computer games and classic board games. But can RL be used in practical, real-world applications? As always it’s good to start out with disclaimers: RL requires a lot of data and simulations, and research results tend to be difficult to reproduce.

However, two things seem point towards the direction of RL applications. First, tools for writing RL models and plugging them into simulators are starting to emerge, and many of them target developers who aren’t experts in machine learning. Secondly, companies are very interested in automation, particularly low-skilled tasks that occupy high-skilled workers. In this context automation is sometimes referred to as robotics process automation or enterprise workflow automation. Many tasks that involve sequential decision making are amenable to learning/training making them ideal candidates for RL based automation solutions. The democratization of tools coupled with the interest in automation (using learning rather than programming and rules), points towards interesting applications of RL in the near future.

We will feature keynotes, talks, and tutorials that will introduce the latest RL tools as well as applications to industrial automation and manufacturing, autonomous vehicles, and software development.

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13:3017:00 Wednesday, April 11, 2018
Arthur Juliani (Unity Technologies), Leon Chen (Unity Technologies)
Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments is deep reinforcement learning. Arthur Juliani and Leon Chen lead a deep dive into reinforcement learning, from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks. Read more.
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10:1510:30 Thursday, April 12, 2018
英文讲话 (Presented in English)
Location: 紫金大厅A(Grand Hall A)
Danny Lange (Unity Technologies)
Danny Lange offers an overview of deep reinforcement learning, an exciting new chapter in AI’s history that is changing the way we develop and test learning algorithms that can later be used in real life. Read more.
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11:1511:55 Thursday, April 12, 2018
实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
Location: 多功能厅3A+B (Function Room 3A+B)
Erran Li (Uber ATG)
深度增强学习已经让人工智能体在很多挑战性的领域可以取得超越人类的表现,例如玩Atari的游戏以及下围棋。这一方法还具有能显著地推进自动驾驶的潜力。Erran Li将会讨论近期在模仿学习方面(例如infoGAIL)、策略梯度法和层次增强学习(例如option-critic架构)等方面的进步,以及它们在自动驾驶方面的应用。Erran接着还会介绍在这个领域需要关注的剩余的挑战。 Read more.
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14:0014:40 Thursday, April 12, 2018
Danny Lange (Unity Technologies)
Danny Lange demonstrates the role games can play in driving the development of reinforcement learning algorithms. Danny uses the Unity Engine with the ML-Agents toolkit as an example of how dynamic 3D game environments can be utilized for machine learning research. Read more.
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09:0509:20 Friday, April 13, 2018
英文讲话 (Presented in English)
Location: 紫金大厅A(Grand Hall A)
Mark Hammond (Bonsai)
Mark Hammond explores a wide breadth of real-world applications of deep reinforcement learning, including robotics, manufacturing, energy, and supply chain. Mark also shares best practices and tips for building and deploying these systems, highlighting the unique requirements and challenges of industrial AI applications. Read more.
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13:1013:50 Friday, April 13, 2018
企业人工智能 (AI in the Enterprise), 英文讲话 (Presented in English)
Location: 多功能厅2(Function Room 2) Level: Intermediate
Mark Hammond (Bonsai)
Mark Hammond dives into two case studies highlighting how deep reinforcement learning can be applied to real-world industrial applications. Read more.
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14:0014:40 Friday, April 13, 2018
实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Location: 多功能厅2(Function Room 2) Level: Intermediate
Arsenii Mustafin (Fudan University)
Deep reinforcement learning is a thriving area and has wide applications in industry. Arsenii Mustafin shares his experience developing deep reinforcement learning applications on BigDL and Spark. Read more.