Monthly Archives: October 2018

Today we are open sourcing a new library of useful building blocks for writing reinforcement learning (RL) agents in TensorFlow. Named TRFL (pronounced truffle), it represents a collection of key algorithmic components that we have used internally for a large number of our most successful agents such as DQN, DDPG and the Importance Weighted Actor Learner Architecture.A typical deep reinforcement learning agent consists of a large number of interacting components: at the very least, these include the environment and some deep network representing values or policies, but they often also include components such as a learned model of the environment, pseudo-reward functions or a replay system.These parts tend to interact in subtle ways (often not well-documented in papers, as highlighted by Henderson and colleagues), thus making it difficult to identify bugs in such large computational graphs. A recent blog post by OpenAI highlighted this issue by analysing some of the most popular open-source implementations of reinforcement learning agents and finding that six out of 10 had subtle bugs found by a community member and confirmed by the author.One approach to addressing this issue, and helping those in the research community attempting to reproduce results from papers, is through open-sourcing complete agent implementations.Read More

Japanese version followsSix months ago, we joined a groundbreaking new research partnership led by the Cancer Research UK Imperial Centre at Imperial College London to explore whether AI technology could help clinicians diagnose breast cancers on mammograms quicker and more effectively.Breast cancer is a huge global health problem. Around the world, over 1.6 million people are diagnosed with the disease every single year, and 500,000 lose their life to it partly because accurately detecting and diagnosing breast cancer still remains a huge challenge.Working alongside leading breast cancer experts, clinicians and academics in the UK, weve been exploring whether machine learning (a form of AI) could help address this issue.Today, were delighted to announce that this project is expanding internationally, with The Jikei University Hospital, one of Japans foremost medical institutions, joining the collaborationas part of a wider five year partnership they have signed with DeepMind Health.For the purposes of this research, they will be working with us to analyse historic, de-identified mammograms from around 30,000 women taken at the hospital between 2007 and 2018.Read More