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Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when the policy network is very large. The predecessor to PPO, Trust Region Policy Optimization (TRPO), was published in 2015.
Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g. every pixel rendered to the screen in a video game) and decide what actions to perform to optimize an objective (e.g ...
The Hugging Face Hub is a platform (centralized web service) for hosting: [19] Git -based code repositories , including discussions and pull requests for projects. models, also with Git-based version control;
A key breakthrough was LSTM (1995), [note 1] a RNN which used various innovations to overcome the vanishing gradient problem, allowing efficient learning of long-sequence modelling. One key innovation was the use of an attention mechanism which used neurons that multiply the outputs of other neurons, so-called multiplicative units. [13]
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal.
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Open-source machine translation models have paved the way for multilingual support in applications across industries. Hugging Face's MarianMT is a prominent example, providing support for a wide range of language pairs, becoming a valuable tool for translation and global communication. [63]
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.