When it comes to research in new-age technologies, Microsoft has been striving hard to stay ahead of its competitors. From recommendations to gaming, the tech
With the success of DeepMind’s AlphaGo system defeating the world Go champion, reinforcement learning has achieved significant attention among researchers and developers. Deep reinforcement learning
One of the popular machine learning techniques, reinforcement learning has been used by various organisations and academia to handle large and complex problems. The technique
“The new infrastructure reduces the training time from eight hours down to merely one hour compared to a strong baseline.” The current state-of-the-art reinforcement learning
The success of deep learning has been linked to how well the algorithms generalised when presented with open-world settings. This notion transformed the fields of
With significant advancements in the field of deep learning, machines are now trained to achieve human-like performances for numerous tasks. Many a time, these AI
Recently, researchers from DeepMind and McGill University proposed new approaches to speed up the solution of complex reinforcement learning problems. They mainly introduced a divide
Recently, researchers from DeepMind and Google introduced methods for choosing the best policy in offline reinforcement learning (ORL) known as offline hyperparameter selection (OHS). It
DeepMind recently introduced a new meta-learning approach that generates a reinforcement learning algorithm known as Learned Policy Gradient (LPG). According to the researchers, automating the
“Empathy, evidently, existed only within the human community, whereas intelligence to some degree could be found throughout every phylum.” ― Philip K. Dick Moral dilemmas
It is well established that machine learning models perform better with well-curated large scale data. However, collecting and curating is one of the biggest challenges
The environment and its underlying dynamics of all the reinforcement learning problems are typically abstracted as a Markov decision process (MDP). Because MDPs are useful
Deep reinforcement learning algorithms are considerably sensitive to implementation details, hyper-parameters, choice of environments, and even random seeds. The variability in the execution can put
Reinforcement learning (RL) algorithm designers often tend to hard code use cases into the system because the nature of the environment in which an agent
Recently, researchers from the Intel Lab and the University of Southern California introduced an AI system known as Sample Factory that can optimise the efficiency
A classical approach to any reinforcement learning (RL) problem is to explore and to exploit. Explore the most rewarding way that reaches the target and
Reinforcement Learning has become the base approach in order to attain artificial general intelligence. The ICLR (International Conference on Learning Representations) is one of the
In this article, we will discuss reinforcement learning in Click-Through-Rate (CTR) prediction of web advertisements. We will see the practical implementation of Upper Confidence Bound (UCB), a method of reinforcement learning applied in this task. Using this implementation, one can be able to find the best version of the advertisement from a set of available versions that can get a maximum number of clicks by the visitors on the website.
From teaching a robot to drive itself off-road to adapting to never-before-seen tasks and grasping occluded objects, University of California, Berkeley, has been investing and
Reinforcement learning is one of the most popular machine learning techniques among organisations to develop solutions like recommendation systems, healthcare, robotics, transportations, among others. This
Reinforcement learning is one of the most happening domains within AI since the early days. The innovations are often ingenious, but we rarely see them
Reinforcement learning is one of the most important techniques used to achieve artificial general intelligence. However, it has various disadvantages that prevent researchers from achieving
A reinforcement learning system consists of four main elements: An agent A policy A reward signal, and A value function An agent’s behaviour at any
For the first time, an AI model has outperformed top players in the game of Mahjong. Microsoft Research Asia designed an AI model for Mahjong
Ever wonder how we instinctively act on a certain situation when we face danger without the need for a conscious plan of action? This is
Games like chess, GO, and Atari have become testbeds of testing deep reinforcement learning algorithms. Companies like DeepMind and OpenAI have done a tremendous amount
Ever heard about financial use cases of reinforcement learning, yes but very few. One such use case of reinforcement learning is in portfolio management. Earlier
The age of algorithmic innovations has now entered a new realm where the researchers are finding flaws in the techniques through adversarial attacks. In the
In Reinforcement learning, the generalization of the agents is benchmarked on the environments they have been trained on. In a supervised learning setting, this would
AI research startup DeepMind has now open-sourced new libraries for neural networks and reinforcement learning based on JAX. JAX is a numerical computing library launched
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