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2017 GTC San Jose

L7137 - Deep Reinforcement Learning for Gameplay and Robotics

Session Speakers
Session Description

In this lab, you will learn the basics of Chainer and how to use ChainerRL by training an agent to play text-based games with OpenAI Gym on a Jupyter notebook. ChainerRL contains a set of Chainer implementations of deep reinforcement learning (DRL) algorithms. Following the success of DeepMind's Deep Q-Network (DQN) algorithm on Atari games, DRL has been applied to many tasks from playing Go to robot control. ChainerRL runs on top of Chainer, one of the popular Python-based deep learning frameworks, which enables users to intuitively implement many kinds of models, with a lot of flexibility and comparable performance with GPUs. ChainerRL already includes state-of-the-art DRL algorithms from DQN to DDPG to A3C, so that users can use them on their reinforcement learning applications. Prerequisites: Basic knowledge of Python, deep learning and reinforcement learning. This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.


Additional Session Information
Intermediate
Instructor-Led Lab
Deep Learning and AI Tools and Libraries
Games Software
2 hours
Session Schedule