Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take...
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Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem...
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In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent to human preferences. It involves training...
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Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that...
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In reinforcement learning (RL), a model-free algorithm (as opposed to a model-based one) is an algorithm which does not estimate the transition probability...
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Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the...
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signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize...
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Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves...
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Machine learning is commonly separated into three main learning paradigms, supervised learning, unsupervised learning and reinforcement learning. Each corresponds...
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Softmax function (section Reinforcement learning)
model which uses the softmax activation function. In the field of reinforcement learning, a softmax function can be used to convert values into action probabilities...
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absence of motor reproduction or direct reinforcement. In addition to the observation of behavior, learning also occurs through the observation of rewards...
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systems where there's no evident labeling or mapping of components. Reinforcement learning is employed to build models that progressively refine their system...
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Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate...
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Operant conditioning (redirect from Operant learning)
stimuli. The frequency or duration of the behavior may increase through reinforcement or decrease through punishment or extinction. Operant conditioning originated...
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with reinforcement learning, such as learning a simplified version of a game first. Some domains have shown success with anti-curriculum learning: training...
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extended this approach to optimization in 2017. In the 1990s, Meta Reinforcement Learning or Meta RL was achieved in Schmidhuber's research group through...
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Artificial intelligence (redirect from Probabilistic machine learning)
Supervised learning: Russell & Norvig (2021, §19.2) (Definition) Russell & Norvig (2021, Chpt. 19–20) (Techniques) Reinforcement learning: Russell & Norvig...
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performance of reinforcement learning agents in the projective simulation framework. Reinforcement learning is a branch of machine learning distinct from...
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of fully self-contained autoencoder training. In reinforcement learning, self-supervising learning from a combination of losses can create abstract representations...
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one for losing. Reinforcement learning is used heavily in the field of machine learning and can be seen in methods such as Q-learning, policy search,...
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Multi-objective reinforcement learning (MORL) is a form of reinforcement learning concerned with conflicting alternatives. It is distinct from multi-objective...
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Proximal policy optimization (category Reinforcement learning)
Proximal policy optimization (PPO) is an algorithm in the field of reinforcement learning that trains a computer agent's decision function to accomplish difficult...
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unlabeled data Reinforcement learning - where the model learns to make decisions by receiving rewards or penalties Applications of machine learning Bioinformatics...
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OpenAI (section Reinforcement learning)
OpenAI released a public beta of "OpenAI Gym", its platform for reinforcement learning research. Nvidia gifted its first DGX-1 supercomputer to OpenAI...
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application of MDP process in machine learning theory is called learning automata. This is also one type of reinforcement learning if the environment is stochastic...
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systems without significant simplification and robustification. Reinforcement learning algorithms, in particular, require measuring their performance over...
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conversational applications using a combination of supervised learning and reinforcement learning from human feedback. ChatGPT was released as a freely available...
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naturally produces gradient-based primal-dual algorithms in safe reinforcement learning. Considering the PDE problems with constraints, i.e., the study...
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Infinite Mario which used reinforcement learning, and Frogger II, Space Invaders, and Fast Eddie, which used both reinforcement learning and mental imagery....
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In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...
52 KB (6,613 words) - 13:09, 22 June 2024