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Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent's decision function to accomplish difficult tasks. PPO was developed by John Schulman in 2017, [ 1 ] and had become the default RL algorithm at the US artificial intelligence company OpenAI . [ 2 ]
Model-free RL algorithms can start from a blank policy candidate and achieve superhuman performance in many complex tasks, including Atari games, StarCraft and Go.Deep neural networks are responsible for recent artificial intelligence breakthroughs, and they can be combined with RL to create superhuman agents such as Google DeepMind's AlphaGo.
From the theory of Markov decision processes it is known that, without loss of generality, the search can be restricted to the set of so-called stationary policies. A policy is stationary if the action-distribution returned by it depends only on the last state visited (from the observation agent's history).
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The Washington Post has lost at least 250,000 subscribers since announcing last Friday that it would not endorse a candidate for president — roughly 10 percent of its digital following, the ...
The Wasserstein Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches".
HMO. Health Maintenance Organization plans are often considered the most affordable insurance option. With low deductibles and low copays for doctor visits and pharmaceuticals, HMOs are affordable ...
April 12: Recall over loss of drive power from low battery. Ford recalled certain 2021-2024 Bronco Sport and 2022-2023 Maverick vehicles. In the NHTSA report, the company said the body and power ...