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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 ...
Learning can happen either through autonomous self-exploration or through guidance from a human teacher, like for example in robot learning by imitation. Robot learning can be closely related to adaptive control , reinforcement learning as well as developmental robotics which considers the problem of autonomous lifelong acquisition of ...
Various techniques exist to train policies to solve tasks with deep reinforcement learning algorithms, each having their own benefits. At the highest level, there is a distinction between model-based and model-free reinforcement learning, which refers to whether the algorithm attempts to learn a forward model of the environment dynamics.
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 .
Exploration in artificial intelligence and robotics has been extensively studied in reinforcement learning models, [12] usually by encouraging the agent to explore as much of the environment as possible, to reduce uncertainty about the dynamics of the environment (learning the transition function) and how best to achieve its goals (learning the ...
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 a 2004 paper, a reinforcement learning algorithm was designed to encourage a physical Mindstorms robot to remain on a marked path. Because none of the robot's three allowed actions kept the robot motionless, the researcher expected the trained robot to move forward and follow the turns of the provided path.
Reinforcement learning – an area of machine learning in computer science, concerned with how an agent ought to take actions in an environment so as to maximize some notion of cumulative reward. Robot kinematics – applies geometry to the study of the movement of multi-degree of freedom kinematic chains that form the structure of robotic systems.