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Field of machine learning
In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment
Reinforcement_learning
Machine learning that combines deep learning and reinforcement learning
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem
Deep_reinforcement_learning
Sub-field of reinforcement learning
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that
Multi-agent reinforcement learning
Multi-agent_reinforcement_learning
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Computational model used in machine learning
Machine learning has involved a variety of approaches to training models, including supervised learning, unsupervised learning, reinforcement learning, and
Neural network (machine learning)
Neural_network_(machine_learning)
Model-free reinforcement learning algorithm
Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring
Q-learning
Algorithm for modelling sequential data
processing, computer vision (vision transformers), reinforcement learning, audio, multimodal learning, robotics, and playing chess. It has also led to the
Transformer_(deep_learning)
Academic conference in machine learning
machine learning conferences NeurIPS and ICLR, ICML traditionally features more content on statistical learning theory, reinforcement learning and robotics
International Conference on Machine Learning
International_Conference_on_Machine_Learning
revolutionized the study of reinforcement learning and decision making over the past four decades. In 1988, Sutton described machine learning in terms of decision
History of artificial intelligence
History_of_artificial_intelligence
Type of associative learning process for behavioral modification
stimuli. The frequency or duration of the behavior may increase through reinforcement or decrease through punishment or extinction. Operant conditioning originated
Operant_conditioning
Subset of artificial intelligence
against top human players in Go using reinforcement learning techniques. As a scientific endeavour, machine learning grew out of the quest for artificial
Machine_learning
and tools used for machine learning, deep learning, natural language processing, computer vision, reinforcement learning, artificial general intelligence
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
Smooth approximation of one-hot arg max
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
Softmax_function
Mathematical model for sequential decision making under uncertainty
telecommunications and reinforcement learning. Reinforcement learning utilizes the MDP framework to model the interaction between a learning agent and its environment
Markov_decision_process
American computer scientist and professor (born 1948)
foundational contributions to the field of modern computational reinforcement learning. Andrew Gehret Barto was born in 1948. He received his B.S. with
Andrew_Barto
Computer scientist
founders of modern computational reinforcement learning. In particular, he contributed to temporal difference learning and policy gradient methods. He
Richard_S._Sutton
Type of large language model
trained for. Following the release of GPT-3, OpenAI started using reinforcement learning from human feedback (RLHF) to align models' behavior more closely
Generative pre-trained transformer
Generative_pre-trained_transformer
2023 text-generating language model
human reviews are used to fine-tune the system in a process called reinforcement learning from human feedback, which trains the model to refuse prompts which
GPT-4
Class of reinforcement learning algorithm
In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward
Model-free (reinforcement learning)
Model-free_(reinforcement_learning)
Type of machine learning model
techniques like reinforcement learning from human feedback (RLHF) or constitutional AI. Instruction fine-tuning is a form of supervised learning used to teach
Large_language_model
Representation learning technique
extraction Dimensionality reduction Word embedding Neural network Reinforcement learning Metric space#Metric embeddings and approximations Bengio, Yoshua;
Embedding_(machine_learning)
Intelligence of machines
humans involved. These preferences be learned (e.g., with inverse reinforcement learning), or the agent can seek information to improve them. Information
Artificial_intelligence
Theory of learning and behaviour
even without physical practice or direct reinforcement. In addition to the observation of behavior, learning also occurs through the observation of rewards
Social_learning_theory
Chinese artificial intelligence company
report on the Kimi K1.5 model, Moonshot researchers outline their reinforcement learning methods, which they claim enabled the model to achieve state-of-the-art
Moonshot_AI
System to predict users' preferences
contrast to traditional learning techniques which rely on supervised learning approaches that are less flexible, reinforcement learning recommendation techniques
Recommender_system
Machine learning-powered structure design
hyperparameter optimization and meta-learning and is a subfield of automated machine learning (AutoML). Reinforcement learning (RL) can underpin a NAS search
Neural_architecture_search
Language models designed for reasoning tasks
reported that o1's accuracy improves as the model is given more reinforcement learning during training and more test-time compute at inference. The company
Reasoning_model
Machine learning technique where agents learn from demonstrations
Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations
Imitation_learning
AI research laboratory
The company has created many neural network models trained with reinforcement learning to play video games and board games. It made headlines in 2016 after
Google_DeepMind
Computer scientist and researcher
From 2013 to 2026, Silver worked full-time at DeepMind, leading reinforcement learning research. He notably led the development of AlphaGo and AlphaZero
David Silver (computer scientist)
David_Silver_(computer_scientist)
Class of reinforcement learning algorithms
Policy gradient methods are a class of reinforcement learning algorithms and a sub-class of policy optimization methods. Unlike value-based methods which
Policy_gradient_method
Technique for the generative modeling of a continuous probability distribution
such as text generation and summarization, sound generation, and reinforcement learning. Diffusion models were introduced in 2015 as a method to train a
Diffusion_model
Machine learning methods using multiple input modalities
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images
Multimodal_learning
American software company
2026. According to the company, the model was produced by scaling reinforcement learning on the same pretrained model used for Composer. In March 2026, Cursor
Cursor_(company)
Large language model by Meta AI
larger but lower-quality third-party datasets. For AI alignment, reinforcement learning with human feedback (RLHF) was used with a combination of 1,418
Llama_(language_model)
Concept in artificial intelligence
Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves
Apprenticeship_learning
Deep learning architecture
Mamba is a deep learning architecture focused on sequence modeling. It was developed by two researchers Albert Gu from Carnegie Mellon University and Tri
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Technique in machine learning
with reinforcement learning, such as learning a simplified version of a game first. Some domains have shown success with anti-curriculum learning: training
Curriculum_learning
Type of feedforward neural network
In deep learning, a multilayer perceptron (MLP) is a kind of modern feedforward neural network consisting of fully connected neurons with nonlinear activation
Multilayer_perceptron
Subfield of machine learning
extended this approach to optimization in 2017. In the 1990s, Meta Reinforcement Learning or Meta RL was achieved in Schmidhuber's research group through
Meta-learning (computer science)
Meta-learning_(computer_science)
Artificial intelligence concept
hacking or specification gaming occurs when an AI trained with reinforcement learning optimizes an objective function—achieving the literal, formal specification
Reward_hacking
Conformance of AI to intended objectives
judges most likely to attain the maximum value of +1. Similarly, a reinforcement learning system can have a "reward function" that allows the programmers
AI_alignment
Mechanism for enabling artificial agents to exhibit curiosity
Intrinsic motivation is often studied in the framework of computational reinforcement learning (introduced by Sutton and Barto), where the rewards that drive agent
Intrinsic motivation (artificial intelligence)
Intrinsic_motivation_(artificial_intelligence)
Type of database that uses vectors to represent other data
from the raw data using machine learning methods such as feature extraction algorithms, word embeddings or deep learning networks. The goal is that semantically
Vector_database
Model-free reinforcement learning algorithm
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient
Proximal_policy_optimization
American computer scientist and software engineer
2021, Dean was a senior author on a Nature paper introducing a reinforcement-learning approach to chip floorplanning. Dean and co-authors claimed the
Jeff_Dean
2025 multimodal model by OpenAI
three stages: unsupervised pretraining, supervised fine-tuning, and reinforcement learning from human feedback. Pretraining used a large-scale multilingual
GPT-5
System of multiple interacting agents
methodic, functional, procedural approaches, algorithmic search or reinforcement learning. With advancements in large language models (LLMs), LLM-based multi-agent
Multi-agent_system
Computer programming concept
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate
Temporal_difference_learning
Paradigm of rule-based machine learning methods
computation) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems
Learning_classifier_system
Canadian technology company
generation. Maluuba published a research paper learning dialogue policies with deep reinforcement learning. In 2016, Maluuba also freely released the Frames
Maluuba
Research field that lies at the intersection of machine learning and computer security
Ridge regression. Adversarial deep reinforcement learning is an active area of research in reinforcement learning focusing on vulnerabilities of learned
Adversarial_machine_learning
Overview of and topical guide to machine learning
unlabeled data Reinforcement learning, where the model learns to make decisions by receiving rewards or penalties. Applications of machine learning Bioinformatics
Outline_of_machine_learning
Computer scientist and professor
robotics, machine learning, and control. His research has centered on reinforcement learning, imitation learning, and scalable robot learning systems. Levine’s
Sergey_Levine
PMC 346238. PMID 6953413. Bozinovski, S. (1982). "A self-learning system using secondary reinforcement". In Trappl, Robert (ed.). Cybernetics and Systems Research:
Timeline_of_machine_learning
Academic conference in machine learning
The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year.
International Conference on Learning Representations
International_Conference_on_Learning_Representations
Machine-learning and computational-neuroscience conference
in the visual cortex (ConvNet) and reinforcement learning inspired by the basal ganglia (Temporal difference learning). Notable affinity groups have emerged
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
Use of artificial intelligence in the automation of electronic design
Automation uses several methods, including machine learning, expert systems, and reinforcement learning. These are used for many tasks, from planning a chip's
AI-driven_design_automation
American computer scientist and academic
worked on robot learning algorithms from deep predictive models. She delivered a massive open online course on deep reinforcement learning. She was the first
Chelsea_Finn
Type of feedforward neural network
deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike earlier reinforcement learning agents
Convolutional_neural_network
American computer scientist
the University of Washington. Kakade's research includes work on Reinforcement Learning, Tensor-Algebraic methods, and Convex optimization. Kakade's doctoral
Sham_Kakade
2018 text-generating language model
primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets
GPT-1
French AI firm
New York, United States and Paris, France. The company focuses on reinforcement learning (“RLOps”), providing tools that allow organizations to customize
Adaptive_ML
Interdisciplinary research area
the performance of reinforcement learning agents in the projective simulation framework. In quantum-enhanced reinforcement learning, a quantum agent interacts
Quantum_machine_learning
American electrical engineer, academic and researcher
dynamical systems using the new notion of Integral Reinforcement Learning (IRL). This allows the adaptive learning of Optimal control solutions online in real
Frank_L._Lewis
American artificial intelligence researcher
Pennsylvania. Between 1996 and 1998 he also conducted research on reinforcement learning, model selection, and feature selection at the AT&T Bell Labs. In
Andrew_Ng
Symbolic cognitive architecture
Infinite Mario which used reinforcement learning, and Frogger II, Space Invaders, and Fast Eddie, which used both reinforcement learning and mental imagery.
Soar_(cognitive_architecture)
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,
Machine learning in video games
Machine_learning_in_video_games
Game-playing artificial intelligence
planning of the AlphaZero (AZ) algorithm with approaches to model-free reinforcement learning. The combination allows for more efficient training in classical
MuZero
Class of artificial neural network
whose middle layer contains recurrent connections that change by a Hebbian learning rule. Later, in Principles of Neurodynamics (1961), he described "closed-loop
Recurrent_neural_network
Machine learning algorithm
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or
Decision_tree_learning
Serbian-American AI researcher and technology executive
Research at Google DeepMind, where she led scalable autonomy and reinforcement learning research. In 2020, she received the IEEE Early Career Award in Robotics
Aleksandra_Faust
Machine learning researcher at Berkeley
his cutting-edge research in robotics and machine learning, particularly in deep reinforcement learning. In 2021, he joined AIX Ventures as an Investment
Pieter_Abbeel
Inability to feel pleasure
(wanting), reduced consummatory pleasure (liking), and deficits in reinforcement learning. In the Diagnostic and Statistical Manual of Mental Disorders, Fifth
Anhedonia
American entrepreneur
he developed an interest in programming and worked on improving reinforcement learning for simple robotic tasks. He co-created the Halite AI Programming
Michael_Truell
Principle in artificial intelligence
Bitter Lesson of Reinforcement Learning". Proceedings of the 41st International Conference on Machine Learning. Proceedings of Machine Learning Research. Retrieved
Bitter_lesson
Software agent which acts autonomously
expected value of this function upon completion. For example, a reinforcement learning agent has a reward function, which allows programmers to shape its
Intelligent_agent
Cognitive capacity of horses
horses to perform expected tasks. Reinforcement can be positive or negative. At the beginning of reinforcement learning, the horse may be unaware of what
Equine_intelligence
Machine learning technique
"Self-organizing maps for storage and transfer of knowledge in reinforcement learning". Adaptive Behavior. 27 (2): 111–126. arXiv:1811.08318. doi:10
Transfer_learning
Use of machine learning to rank items
Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning
Learning_to_rank
Internal representation of world by AI
self-supervised learning. They use large unlabeled datasets of video or robot interactions. Self-supervised learning can speed learning. Reinforcement learning can
World model (artificial intelligence)
World_model_(artificial_intelligence)
Branch of computer science
systems where there's no evident labeling or mapping of components. Reinforcement learning is employed to build models that progressively refine their system
AI-assisted reverse engineering
AI-assisted_reverse_engineering
Concept in decision-making
context of machine learning, the exploration–exploitation tradeoff is fundamental in reinforcement learning (RL), a type of machine learning that involves
Exploration–exploitation dilemma
Exploration–exploitation_dilemma
Hypothesis about intelligent agents
externalities. The "delusion box" thought experiment argues that certain reinforcement learning agents prefer to distort their input channels to appear to receive
Instrumental_convergence
Chatbot developed by Microsoft
models, which in turn have been fine-tuned using both supervised and reinforcement learning techniques. Copilot's conversational interface style resembles that
Microsoft_Copilot
British-American computer scientist
University faculty in 1993 as an assistant professor in machine learning, reinforcement learning, manufacturing, and non-parametric regression. He received
Andrew_W._Moore
American data annotation company
annotation company based in San Francisco, California. Surge focuses on reinforcement learning from human feedback (RLHF), RL environments, and annotating language
Surge_AI
Scottish philosopher and AI researcher
instructions to do so. The research tested whether models trained with reinforcement learning from human feedback (RLHF) could avoid stereotyping and discrimination
Amanda_Askell
Machine learning strategy
for machine learning research Sample complexity Bayesian optimization Reinforcement learning Improving Generalization with Active Learning, David Cohn
Active learning (machine learning)
Active_learning_(machine_learning)
Machine learning technique
In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence
Attention_(machine_learning)
Reinforcement learning algorithms
The actor-critic algorithm (AC) is a family of reinforcement learning (RL) algorithms that combine policy-based RL algorithms such as policy gradient methods
Actor-critic_algorithm
computer-generated music for stress and pain relief. The Watson Beat uses reinforcement learning and deep belief networks to compose music on a simple seed input
Applications of artificial intelligence
Applications_of_artificial_intelligence
Machine learning technique
supervised learning, but there are also techniques to fine-tune a model using weak supervision. Fine-tuning can be combined with a reinforcement learning from
Fine-tuning_(deep_learning)
Artificial neural network node function
"Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning". Neural Networks. 107: 3–11. arXiv:1702.03118. doi:10.1016/j.neunet
Activation_function
Set of methods for supervised statistical learning
In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that
Support_vector_machine
Algorithm for supervised learning of binary classifiers
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether
Perceptron
Legal and scientific dispute over 2021 Nature paper by Google
placement, a stage of chip floorplanning, based on reinforcement learning (RL), a machine learning method in which a system iteratively improves its decisions
AlphaChip_(controversy)
Property of a model
though the bias–variance decomposition does not directly apply in reinforcement learning, a similar tradeoff can also characterize generalization. When an
Bias–variance_tradeoff
Parameter controlling the machine learning process
systems without significant simplification and robustification. Reinforcement learning algorithms, in particular, require measuring their performance over
Hyperparameter (machine learning)
Hyperparameter_(machine_learning)
Generative AI chatbot by OpenAI
fine-tuning process involved supervised learning and reinforcement learning from human feedback (RLHF). During supervised learning, the trainers acted as both the
ChatGPT
REINFORCEMENT LEARNING
REINFORCEMENT LEARNING
Boy/Male
American, Anglo, Australian, British, Christian, English, French
Steward; A Law Enforcement Officer's Title; Horse-keeper; Steward of Horses; Shoeing Smith
Girl/Female
Tamil
Saraswati | ஸரஸà¯à®µà®¤à¯€
Goddess Saraswati, Tamil Goddess for education, Goddess of learning
Saraswati | ஸரஸà¯à®µà®¤à¯€
Girl/Female
Tamil
Saraswathi | ஸரஸà¯à®µà®¾à®¤à¯€Â
Goddess Saraswati, Tamil Goddess for education, Goddess of learning
Saraswathi | ஸரஸà¯à®µà®¾à®¤à¯€Â
Surname or Lastname
English
English : occupational name for the law-enforcement officer of a parish, from Middle English, Old French conestable, cunestable, from Late Latin comes stabuli ‘officer of the stable’. The title was also borne by various other officials during the Middle Ages, including the chief officer of the household (and army) of a medieval ruler, and this may in some cases be the source of the surname.Americanized spelling of Dutch Constapel, an occupational name for the chief gunner aboard a ship or in the garrison of a fort.
Boy/Male
Tamil
Vidyasagar | விதà¯à®¯à®¾à®¸à®¾à®•à®°Â
Ocean of learning
Vidyasagar | விதà¯à®¯à®¾à®¸à®¾à®•à®°Â
Girl/Female
Tamil
Goddess of learning, Saraswati
Girl/Female
Tamil
Vidhyavathi | விதà¯à®¯à®¾à®µà®¾à®¤à¯€
Wisdom, Knowledge, Learning, Goddess Durga
Vidhyavathi | விதà¯à®¯à®¾à®µà®¾à®¤à¯€
Surname or Lastname
English, French, German, Hungarian (Donát), Polish, and Czech (Donát)
English, French, German, Hungarian (Donát), Polish, and Czech (Donát) : from a medieval personal name (Latin Donatus, past participle of donare, frequentative of dare ‘to give’). The name was much favored by early Christians, either because the birth of a child was seen as a gift from God, or else because the child was in turn dedicated to God. The name was borne by various early saints, among them a 6th-century hermit of Sisteron and a 7th-century bishop of Besançon, all of whom contributed to the popularity of the baptismal name in the Middle Ages, which was not checked by the heresy of a 4th-century Carthaginian bishop who also bore it. Another bearer was a 4th-century gramMarian and commentator on Virgil, widely respected in the Middle Ages as a figure of great learning.
Boy/Male
Arabic, Hindu, Indian, Kannada, Marathi, Muslim, Telugu
Reinforcement
Girl/Female
Tamil
Goddess of learning, Saraswati
Boy/Male
Tamil
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Learning ocean
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Girl/Female
Tamil
Goddess of learning, Saraswati
Girl/Female
Tamil
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Knowledge, Learning
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Boy/Male
English American French
Steward. Also, a law enforcement officer's title.
Girl/Female
Tamil
Vaagdevi | வாகà¯à®¤à¯‡à®µà¯€
Goddess of learning, Saraswati
Vaagdevi | வாகà¯à®¤à¯‡à®µà¯€
Girl/Female
Tamil
Saraswathy | ஸரஸà¯à®µà®¾à®¤à¯€ Â
Goddess Saraswati, Tamil Goddess for education, Goddess of learning
Saraswathy | ஸரஸà¯à®µà®¾à®¤à¯€ Â
Girl/Female
Tamil
Vidyasri | விதà¯à®¯à®¾à®¸à®°à¯€
Wisdom, Knowledge, Learning, Goddess Durga
Vidyasri | விதà¯à®¯à®¾à®¸à®°à¯€
Girl/Female
Tamil
Learning
Girl/Female
Tamil
Sarasvati | ஸரஸà¯à®µà®¤à¯€
A Goddess of learning
Sarasvati | ஸரஸà¯à®µà®¤à¯€
Boy/Male
Muslim
Reinforcement
REINFORCEMENT LEARNING
REINFORCEMENT LEARNING
Boy/Male
Bengali, Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Sanskrit
A Year
Boy/Male
Hindu, Indian, Marathi
Singing Gods in Heaven
Surname or Lastname
English
English : from the Middle English and Old English personal name Brūning, originally a patronymic from the byname Brūn (see Brown).This name was brought independently to North America from England by numerous different bearers from the 17th century onward. William Browning was one of the free planters who assented to the ‘Fundamental Agreement’ of the New Haven Colony on June 4, 1639.
Boy/Male
Hindu
More attractive, Gratitude, Thankfulness, Obligation
Boy/Male
Hindu, Indian, Malayalam, Marathi
Hundred Years Old
Male
German
 Pet form of German Ulrich, UTZ means "prosperity and power." Compare with another form of Utz.
Girl/Female
Hindu, Indian, Kannada, Malayalam, Marathi, Telugu
Soft
Girl/Female
Tamil
Hemasri | ஹேமாஂஷà¯à®°à¯€, ஹேமாஂஸரீ, ஹேமாஂஷà¯à®°à¯€Â
One with golden body
Girl/Female
Indian, Tamil
Smartness
Girl/Female
Native American
Fawn.
REINFORCEMENT LEARNING
REINFORCEMENT LEARNING
REINFORCEMENT LEARNING
REINFORCEMENT LEARNING
REINFORCEMENT LEARNING
n.
A piece of armor for the breast; often, an addition to, or reenforcement of. the breastplate; -- called also pesane.
n.
The act of reenforcing, or the state of being reenforced.
n.
A sudden swelling or reenforcement of a sound, recurring at regular intervals, and produced by the interference of sound waves of slightly different periods of vibrations; applied also, by analogy, to other kinds of wave motions; the pulsation or throbbing produced by the vibrating together of two tones not quite in unison. See Beat, v. i., 8.
n.
The concluding part of an oration; especially, a final summing up and enforcement of an argument.
a.
Abiding the law; waiting for the operation of law for the enforcement of rights; also, abiding by the law; obedient to the law; as, law-abiding people.
n.
Auxiliary troops or reenforcements.
n.
An action at law; a suit in equity or admiralty; any legal proceeding before a court for the enforcement of a claim.
n.
That which reenforces; additional force; especially, additional troops or force to augment the strength of any army, or ships to strengthen a navy or fleet.
n.
The act of enforcing; compulsion.
n.
That which maintains or preserves from being overcome, falling, yielding, sinking, giving way, or the like; subsistence; maintenance; assistance; reenforcement; as, he gave his family a good support, the support of national credit; the assaulting column had the support of a battery.
n.
A reenforcement; a strengthening.
n.
The organized body of civil officers in a city, town, or district, whose particular duties are the preservation of good order, the prevention and detection of crime, and the enforcement of the laws.
n.
That which enforces, constraints, gives force, authority, or effect to; constraint; force applied.
n.
A supply of anything wasted or exhausted; a reenforcement.
n.
See Reenforcement.
n.
The enforcement of methods of correction against one guilty of ecclesiastical offenses; reformatory or penal action toward a church member.
n.
A judicial and executive system, for the government of a city, town, or district, for the preservation of rights, order, cleanliness, health, etc., and for the enforcement of the laws and prevention of crime; the administration of the laws and regulations of a city, incorporated town, or borough.
v. t.
To remove from legal jurisdiction or enforcement; as, to outlaw a debt or claim; to deprive of legal force.
n.
A giving force to; a putting in execution.
n.
A suit or process, by which a demand is made of a right in a court of justice; in a broad sense, a judicial proceeding for the enforcement or protection of a right, the redress or prevention of a wrong, or the punishment of a public offense.