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Deep reinforcement learning has been used for a diverse set of applications including but not limited to robotics, video games, natural language processing, computer vision, [1] education, transportation, finance and healthcare.
The website discloses that the team is building a universal AI to help robots see, reason, and on the world around them using deep imitation learning and deep reinforcement learning. Currently, in addition to his research, Abbeel teaches upper-division and graduate classes on Artificial Intelligence, Robotics, and Deep Unsupervised Learning. [22]
Machine learning techniques, particularly reinforcement learning and deep learning, allow robots to improve their performance over time. Robotics engineers design AI models that enable robots to learn from their experiences, optimizing control strategies and decision-making processes.
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. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations. It is also called learning from demonstration and apprenticeship learning .
Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use backpropagation (gradient descent on a neural network) with a fixed topology.
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning .
As a doctoral student she worked as an intern at Google Brain, where she worked on robot learning algorithms from deep predictive models. She delivered a massive open online course on deep reinforcement learning. [5] [6] She was the first woman to win the C.V. & Daulat Ramamoorthy Distinguished Research Award. [7]