We present an empirical study on the use of continual learning (CL) methods in a reinforcement learning (RL) scenario, which, to the best of our knowledge, has not been described before. CL is a very active recent research topic concerned with machine learning under non-stationary data distributions. Although this naturally applies to RL, the use of dedicated CL methods is still uncommon. This may be due to the fact that CL methods often assume a decomposition of CL problems into disjoint sub-tasks of stationary distribution, that the onset of these sub-tasks is known, and that sub-tasks are non-contradictory. In this study, we perform an empirical comparison of selected CL methods in a RL problem where a physically simulated robot must follow a racetrack by vision. In order to make CL methods applicable, we restrict the RL setting and introduce non-conflicting subtasks of known onset, which are however not disjoint and whose distribution, from the learner's point of view, is still non-stationary. Our results show that dedicated CL methods can significantly improve learning when compared to the baseline technique of "experience replay".

Chris Bishop is the director of Microsoft Research Cambridge. The global war for artificial intelligence (AI) talent is raging, with tech giants fighting it out to hire the brightest minds in the field and use them to take their platforms into unchartered waters. There's currently a shortage of people with the skills and experience needed to make breakthroughs in machine learning, a field of computer science that gives machines the ability to learn without being explicitly programmed. Fortunately, many of the top minds in the field are going to be concentrated in one place this week when they descend on a conference in Long Beach, California, called NIPS, which stands for neural information processing systems. Google, Microsoft, DeepMind, Facebook, Intel, Nvidia, Amazon, Apple, and Open AI (Elon Musk's AI research lab) will all be at NIPS presenting their latest research and looking to hire people from rival firms, as well as PhD students fresh out of universities like Stanford, MIT, Oxford, Cambridge, and Imperial.

Some of the brightest minds in machine learning and deep learning are gathered this week in Barcelona for the annual Neural Information Processing Systems (NIPS) conference. This is the 30th year for the NIPS conference, and the main event was sold out well in advance of the opening. That says a lot about the importance of this event for machine learning researchers and data scientists – and the increasing industry focus on this field. For the first time, Intel is a sponsor of this machine learning and deep learning conference. We are showcasing Intel technologies and initiatives that advance artificial intelligence (AI).

In 2005, Hanna Wallach, a machine-learning researcher, found herself bunking with colleagues to attend the Neural Information Systems Processing (NIPS) conference. Wallach had been working in the field since 2001 and had attended numerous conferences, but this was the first time she had roomed with other women who specialized in machine learning, a branch of artificial intelligence that researches how computer programs can learn and grow. As a discipline, it is overwhelmingly male: Wallach estimates that only 13.5% of the entire machine learning field is female. At the conference, Wallach and her roommates, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu, began discussing their experiences and commiserating about the lack of female allies. "We couldn't believe that there were four of us [at the conference]," Wallach says.

In 2005, Hanna Wallach, a machine-learning researcher, found herself bunking with colleagues to attend the Neural Information Systems Processing (NIPS) conference. Wallach had been working in the field since 2001 and had attended numerous conferences, but this was the first time she had roomed with other women who specialized in machine learning, a branch of artificial intelligence that researches how computer programs can learn and grow. As a discipline, it is overwhelmingly male: Wallach estimates that only 13.5% of the entire machine learning field is female. At the conference, Wallach and her roommates, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu, began discussing their experiences and commiserating about the lack of female allies. "We couldn't believe that there were four of us [at the conference]," Wallach says.

In 2005, Hanna Wallach, a machine learning researcher, found herself bunking with colleagues to attend the Neural Information Systems Processing (NIPS) conference. Wallach had been working in the field since 2001 and had attended numerous conferences, but this was the first time she had roomed with other women who specialized in machine learning, a branch of artificial intelligence that researches how computer programs can learn and grow. As a discipline, it is overwhelmingly male: Wallach estimates that only 13.5% of the entire machine learning field is female. At the conference, Wallach and her roommates, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu, began discussing their experiences and commiserating about the lack of female allies. "We couldn't believe that there were four of us [at the conference]," Wallach says.