reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Q-learning at its...

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent to human preferences. It involves training...

Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that...

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...

signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize...

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...

Machine learning is commonly separated into three main learning paradigms, supervised learning, unsupervised learning and reinforcement learning. Each corresponds...

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...

Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves...

stimuli. The frequency or duration of the behavior may increase through reinforcement or decrease through punishment or extinction. Operant conditioning originated...

Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate...

systems where there's no evident labeling or mapping of components. Reinforcement learning is employed to build models that progressively refine their system...

natural language processing, computer vision (vision transformers), reinforcement learning, audio, multi-modal processing, robotics, and even playing chess...

absence of motor reproduction or direct reinforcement. In addition to the observation of behavior, learning also occurs through the observation of rewards...

extended this approach to optimization in 2017. In the 1990s, Meta Reinforcement Learning or Meta RL was achieved in Schmidhuber's research group through...

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,...

with reinforcement learning, such as learning a simplified version of a game first. Some domains have shown success with anti-curriculum learning: training...

computation) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems...

performance of reinforcement learning agents in the projective simulation framework. Reinforcement learning is a branch of machine learning distinct from...

Multi-objective reinforcement learning (MORL) is a form of reinforcement learning concerned with conflicting alternatives. It is distinct from multi-objective...

of fully self-contained autoencoder training. In reinforcement learning, self-supervising learning from a combination of losses can create abstract representations...

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...

Starting in 2013, significant progress was made following the deep reinforcement learning approach, including the development of programs that can learn to...

unlabeled data Reinforcement learning - where the model learns to make decisions by receiving rewards or penalties Applications of machine learning Bioinformatics...

OpenAI released a public beta of "OpenAI Gym", its platform for reinforcement learning research. Nvidia gifted its first DGX-1 supercomputer to OpenAI...

Proximal policy optimization (PPO) is an algorithm in the field of reinforcement learning that trains a computer agent's decision function to accomplish difficult...

Multimodal learning, in the context of machine learning, is a type of deep learning using multiple modalities of data, such as text, audio, or images....

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

systems without significant simplification and robustification. Reinforcement learning algorithms, in particular, require measuring their performance over...