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We propose behavior-driven optimization via Wasserstein distances (WDs) to improve several classes of state-of-the-art reinforcement learning (RL) algorithms. We show that WD regularizers acting on appropriate policy embeddings efficiently incorporate behavioral characteristics into policy optimization. We demonstrate that they improve Evolution Strategy methods by encouraging more efficient exploration, can be applied in imitation learning and to speed up training of Trust Region Policy Optimization methods. Since the exact computation of WDs is expensive, we develop approximate algorithms based on the combination of different methods: dual formulation of the optimal transport problem, alternating optimization and random feature maps, to effectively replace exact WD computations in the RL tasks considered. We provide theoretical analysis of our algorithms and exhaustive empirical evaluation in a variety of RL settings.
The algorithm that makes is stuff work is called a generative adversarial network (which is the long way of writing GAN, for those of you still stuck in machine learning acronym land), and over the last few years, there have been more innovations dedicated to making it work than there have been privacy scandals at Facebook.
Machine learning is an application of AI. It offers a system the ability to learn automatically and improve based on experience. In other words, machine learning is the process in which a machine can learn by itself, without explicitly being programmed. AI and machine learning has risen to the top of many conversations these days. It is indeed an interesting field to be studying, whether you are just playing around with your hobby ideas or getting into more serious and professional applications.
On 23 June 2016, the United Kingdom held a referendum, whether the British people prefer to stay in the European Union or leave. In this referendum, the people voted 51.9% supporting leaving the EU. As a result, the Government invoked Article 50 of the Treaty on European Union, starting a two-year process which was due to conclude with the UK’s exit on 29 March 2019. This process is since referred to as “Brexit”, which is used as a shorthand way of saying the UK leaving the EU – merging the words Britain and exit to get Brexit.
Machine learning is a hot topic in research and industry, with new methodologies developed all the time. The speed and complexity of the field makes keeping up with new techniques difficult even for experts — and potentially overwhelming for beginners.