Archives for reinforcement learning - Page 13
Reinforcement learning has exceeded human-level performance when it comes to playing games. Games as a testbed have rich and challenging domains for testing reinforcement learning algorithms that start with a collection of games and well-known reinforcement learning implementations. Reinforcement learning is beneficial when we need an agent to perform a specific task, but to be…
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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 major AI conferences that take place every year. With more than 600 interesting research papers, there are around 44 research papers in reinforcement learning that have been accepted in this…
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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.
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Reinforcement Learning With Augmented Data Is So Superior It Beats Google & DeepMind Hands-down
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 doing a number of researches around self-learning techniques. Recently, researchers at UC Berkeley open-sourced reinforcement learning with Augmented Data (RAD). It is a simple plug-and-play module that can be used…
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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 learning technique follows the “trial and error” method and interacts with the environment to learn an optimal policy for gaining maximum rewards by making the right decisions. In this article,…
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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 in the real world. Robotics is one area where reinforcement learning is widely used, where robots usually learn novel behaviours through trial and error interactions. However, there are challenges posed…
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A team of researchers from IIT Bombay and Microsoft Research have used reinforcement learning to find approaches that would help figure out the right policies during lockdowns. The researchers have published their work in a paper titled ’ Optimizing Lockdown Policies for Epidemic Control using Reinforcement Learning’. Reinforcement learning is a branch of AI that…
The post IIT Bombay Researcher Uses ML To Find Best Lockdown Policy appeared first on Analytics India Magazine.
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 point of time is defined in terms of a policy. A policy is like a blueprint of the connections between perception and action in an environment. In the next section,…
The post On-Policy VS Off-Policy Reinforcement Learning appeared first on Analytics India Magazine.
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 known as Suphx. The researchers evaluated Suphx on the most popular and competitive Mahjong platform, Tenhou, which has more than 3,50,000 active users. The Suphx model has exhibited higher performance…
The post After Poker And Go, Reinforcement Learning Is Now Beating Mahjong Players appeared first on Analytics India Magazine.
Q-Learning is a traditional model-free approach to train Reinforcement Learning agents. It is also viewed as a method of asynchronous dynamic programming. It was introduced by Watkins&Dayan in 1992. Overview In Q-Learning we build a Q-Table to store Q values for all possible combinations of state and action pairs. It is called Q-Learning because it…
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