Archives for reinforcement learning models
DQN or Deep-Q Networks were first proposed by DeepMind back in 2015 in an attempt to bring the advantages of deep learning to reinforcement learning(RL), Reinforcement learning focuses on training agents to take any action at a particular stage in an environment to maximise rewards. Reinforcement learning then tries to train the model to improve…
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DQN or Deep-Q Networks were first proposed by DeepMind back in 2015 in an attempt to bring the advantages of deep learning to reinforcement learning(RL), Reinforcement learning focuses on training agents to take any action at a particular stage in an environment to maximise rewards. Reinforcement learning then tries to train the model to improve…
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Recently, a team of researchers from Google Brain, Vector Institute and the University of Toronto showed that the entropy, information gain, and empowerment of reinforcement learning agents correlate strongly with a human behaviour similarity metric. In the past few years, reinforcement learning has made several achievements in solving complex problems. From playing complex mind games…
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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 giant has been using popular techniques like reinforcement learning to create efficient products for customers that match their interests. The foundational work in reinforcement learning (RL) started back in 1992,…
The post Microsoft Is Going Big On Reinforcement Learning. Here’s How appeared first on Analytics India Magazine.
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 and conquer approach to reinforcement learning (RL), which is combined with deep learning to scale up the potentials of the agents. For a few years now, reinforcement learning has been…
The post DeepMind Found New Approach To Create Faster Reinforcement Learning Models appeared first on Analytics India Magazine.
Recently, DeepMind collaborated with Columbia University to propose Taylor expansion Policy Optimisation (TayPO), which is a policy optimisation formalism that generalises methods like trust region policy optimisation (TRPO) and improves the performance of several state-of-the-art distributed algorithms. Policy optimisation is one of the main approaches for deriving reinforcement learning algorithms. It has several successful applications…
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Recently, developers from Google Research teamed up with the University of Tokyo to introduce Deployment Efficiency and a model-based algorithm known as Behavior-Regularised Model-ENsemble (BREMEN). The algorithm is said to have the capability to optimise an effective policy offline using much lesser data. Reinforcement Learning is one of the most trending techniques that have been…
The post Google’s New Algorithm Increases Deployment Efficiency With Low Costs In RL Algorithms appeared first on Analytics India Magazine.
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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