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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
Model-free reinforcement learning algorithm
policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method,
Proximal_policy_optimization
Optimization algorithm
Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate
Gradient_descent
Machine learning technique
who write both the prompts and responses. The second step uses a policy gradient method to the reward model. It uses a dataset D R L {\displaystyle D_{RL}}
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Field of machine learning
methods. Gradient-based methods (policy gradient methods) start with a mapping from a finite-dimensional (parameter) space to the space of policies:
Reinforcement_learning
Reinforcement learning algorithms
reinforcement learning (RL) algorithms that combine policy-based RL algorithms such as policy gradient methods, and value-based RL algorithms such as value iteration
Actor-critic_algorithm
Computer scientist
particular, he contributed to temporal difference learning and policy gradient methods. He received the 2024 Turing Award with Andrew Barto. Richard Sutton
Richard_S._Sutton
American computer scientist
introduced the REINFORCE algorithm in 1992, which became the first policy gradient method. Besides his works on neural networks, Williams, together with Wenxu
Ronald_J._Williams
vAttention Perceptron Quasi-Newton method Wake-sleep algorithm Actor-critic algorithm Policy gradient method Proximal policy optimization Q-learning
List of artificial intelligence algorithms
List_of_artificial_intelligence_algorithms
Machine learning model training problem
In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered
Vanishing_gradient_problem
Machine learning technique
resulting algorithm is called gradient-boosted trees; it usually outperforms random forest. As with other boosting methods, a gradient-boosted trees model is
Gradient_boosting
Optimization algorithm
Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e
Stochastic_gradient_descent
Machine-learned bot project using the video game Dota 2
running on 256 GPUs and 128,000 CPU cores, using Proximal Policy Optimization, a policy gradient method. Prior to OpenAI Five, other AI versus human experiments
OpenAI_Five
Theoretical computer scientist
Bedi; Csaba Szepesvari; Mengdi Wang (November 2020). "Variational Policy Gradient Method for Reinforcement Learning with General Utilities" (PDF). Advances
Mengdi_Wang
Topics referred to by the same term
machine learning inspired by behaviorist psychology "REINFORCE", a policy gradient method (often used as PPO) Reinforcement theory in the field of communication
Reinforcement (disambiguation)
Reinforcement_(disambiguation)
Algorithms for solving convex optimization problems
Interior-point methods (also referred to as barrier methods or IPMs) are algorithms for solving linear and non-linear convex optimization problems. IPMs
Interior-point_method
Technique in artificial intelligence
One example is Group Relative Policy Optimization (GRPO), used in DeepSeek-R1, a variant of policy gradient methods that eliminates the need for a separate
Feedback_neural_network
Overview of and topical guide to algorithms
State–action–reward–state–action (SARSA) Temporal difference learning Policy gradient method Actor–critic algorithm Deep reinforcement learning AlphaGo AlphaGo
Outline_of_algorithms
Language models designed for reasoning tasks
recent systems use policy-gradient methods such as Proximal Policy Optimization (PPO) for this reason, as PPO constrains each policy update with a clipped
Reasoning_model
Recurrent neural network architecture
advantageous to train (parts of) an LSTM by neuroevolution or by policy gradient methods, especially when there is no "teacher" (that is, training labels)
Long_short-term_memory
Sustainable energy from sea and river water
power from salinity gradient. One method to utilize salinity gradient energy is called pressure-retarded osmosis. In this method, seawater is pumped into
Osmotic_power
Study of mathematical algorithms for optimization problems
this method reduces to the gradient method, which is regarded as obsolete (for almost all problems). Quasi-Newton methods: Iterative methods for medium-large
Mathematical_optimization
Sequential model-based optimization of expensive black-box functions
Frazier, Peter; Powell, Warren; Dayanik, Savas (2009). "The Knowledge-Gradient Policy for Correlated Normal Beliefs". INFORMS Journal on Computing. 21 (4):
Bayesian_optimization
Method to solve constrained optimization problems
Kaiqing; Jovanovic, Mihailo; Basar, Tamer (2020). Natural policy gradient primal-dual method for constrained Markov decision processes. Advances in Neural
Lagrange_multiplier
Field of engineering
employed classical gradient-based methods to structural optimization problems. The method of usable feasible directions, Rosen's gradient projection (generalized
Multidisciplinary design optimization
Multidisciplinary_design_optimization
Optimization algorithm for artificial neural networks
In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is
Backpropagation
Method of machine learning
for example, stochastic gradient descent. When combined with backpropagation, this is currently the de facto training method for training artificial neural
Online_machine_learning
Problem optimization method
programming (DP) is both a mathematical optimization method and an algorithmic paradigm. The method was developed by Richard Bellman in the 1950s and has
Dynamic_programming
Ensemble learning method
(bagging) Cascading CoBoosting Logistic regression Maximum entropy methods Gradient boosting Margin classifiers Cross-validation List of datasets for machine
Boosting_(machine_learning)
Technique used in stochastic gradient variational inference
The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational
Reparameterization_trick
Set of methods for supervised statistical learning
traditional gradient descent (or SGD) methods can be adapted, where instead of taking a step in the direction of the function's gradient, a step is taken
Support_vector_machine
Representation learning method
apply a widespread stochastic gradient descent method with iterative projection to solve this problem. The idea of this method is to update the dictionary
Sparse_dictionary_learning
Class of reinforcement learning algorithm
Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), Asynchronous Advantage Actor-Critic (A3C), Deep Deterministic Policy Gradient (DDPG)
Model-free (reinforcement learning)
Model-free_(reinforcement_learning)
Family of iterative methods
the gradient. In some special cases when either IPA or likelihood ratio methods are applicable, then one is able to obtain an unbiased gradient estimator
Stochastic_approximation
Parameter controlling the machine learning process
due to high variance. Some reinforcement learning methods, e.g. DDPG (Deep Deterministic Policy Gradient), are more sensitive to hyperparameter choices than
Hyperparameter (machine learning)
Hyperparameter_(machine_learning)
Technique for setting initial values of trainable parameters in a neural network
weight initialization method affects the speed of convergence, the scale of neural activation within the network, the scale of gradient signals during backpropagation
Weight_initialization
Method of improving artificial neural network
first-order training method. If the shift introduced by the changes in previous layers is small, then the correlation between the gradients would be close to
Batch_normalization
Method used to normalize the range of independent variables
final distance. Another reason why feature scaling is applied is that gradient descent converges much faster with feature scaling than without it. It's
Feature_scaling
Theorem of probability theory
This form has applications in Stein variational gradient descent and Stein variational policy gradient. The univariate probability density function for
Stein's_lemma
Prediction model used in Engineering
Gradient-enhanced kriging (GEK) is a surrogate modeling technique used in engineering. A surrogate model (alternatively known as a metamodel, response
Gradient-enhanced_kriging
Overview of and topical guide to machine learning
Predictive learning Preference learning Proactive learning Proximal gradient methods for learning Semantic analysis Similarity learning Sparse dictionary
Outline_of_machine_learning
Mathematical optimization problem restricted to integers
the branch and bound method. For example, the branch and cut method that combines both branch and bound and cutting plane methods. Branch and bound algorithms
Integer_programming
Angle to the horizontal plane
The grade (US) or gradient (UK) (also called slope, incline, mainfall, pitch or rise) of a physical feature, landform or constructed line is either the
Grade_(slope)
Mathematical model for sequential decision making under uncertainty
Lagrangian-based algorithms have been developed. Natural policy gradient primal-dual method. There are a number of applications for CMDPs. It has recently
Markov_decision_process
Method in natural language processing
co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in terms of the context in which words appear
Word_embedding
Fracturing bedrock by pressurized liquid
perforations), to exceed that of the fracture gradient (pressure gradient) of the rock. The fracture gradient is defined as pressure increase per unit of
Fracking
Research field that lies at the intersection of machine learning and computer security
(by no means an exhaustive list). Gradient-based evasion attack Fast Gradient Sign Method (FGSM) Projected Gradient Descent (PGD) Carlini and Wagner (C&W)
Adversarial_machine_learning
Military strategy
Beans). Ríos Montt's policies resulted in the death of thousands, most of them indigenous Mayans. The Indonesian military used the method during the Indonesian
Scorched_earth
Deep learning method
(only small steps are considered in gradient descent) to improve its payoff, it does not even try. One important method for solving this problem is the Wasserstein
Generative adversarial network
Generative_adversarial_network
Computer vision framework
_{i=1}^{n}\nabla E_{\text{snake}}({\bar {v}}_{i}).} Gradient approximation can be done through any finite approximation method with respect to s, such as Finite difference
Active_contour_model
Optimization technique
problems. Their use is always of interest when exact or other (approximate) methods are not available or are not expedient, either because the calculation
Metaheuristic
Technique for the generative modeling of a continuous probability distribution
Brownian walker) and gradient descent down the potential well. The randomness is necessary: if the particles were to undergo only gradient descent, then they
Diffusion_model
Machine learning that combines deep learning and reinforcement learning
policy optimization techniques, play a crucial role in this adaptability. Models like deep deterministic policy gradient (DDPG), and proximal policy optimization
Deep_reinforcement_learning
Iterative method for finding maximum likelihood estimates in statistical models
Dempster, Laird, and Rubin. Other methods exist to find maximum likelihood estimates, such as gradient descent, conjugate gradient, or variants of the Gauss–Newton
Expectation–maximization algorithm
Expectation–maximization_algorithm
Computer compiler optimization technique
the "global" approach, which operates over the whole compilation unit (a method or procedure for instance). Graph-coloring allocation is the predominant
Register_allocation
Class of artificial neural network
non-linear activation functions are differentiable. The standard method for training RNN by gradient descent is the "backpropagation through time" (BPTT) algorithm
Recurrent_neural_network
Scientology teaching method by L. Ron Hubbard
barriers that prevent students from learning: "absence of mass", too steep a gradient, and the misunderstood word. According to Hubbard, each barrier produces
Study_Technology
Reverse-engineering neural networks
features and circuits within models, while the broader field tended towards gradient-based approaches like saliency maps. Before circuit analysis, work in the
Mechanistic_interpretability
Class of algorithms for pattern analysis
analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. The general
Kernel_method
Machine learning and applied statistics
the method of conjugate gradients, Nordsieck methods, Gaussian quadrature rules, and quasi-Newton methods. In all these cases, the classic method is based
Probabilistic_numerics
Deep learning generative model to encode data representation
\phi }{\operatorname {argmax} }}\,L_{\theta ,\phi }(x)} the typical method is gradient descent. It is straightforward to find ∇ θ E z ∼ q ϕ ( ⋅ | x ) [ ln
Variational_autoencoder
Structuring text as input to generative artificial intelligence
(2023). "Automatic Prompt Optimization with "Gradient Descent" and Beam Search". Conference on Empirical Methods in Natural Language Processing: 7957–7968
Prompt_engineering
Study of viruses
salts that form a density gradient, from low to high, in the tube during the centrifugation. In some cases, preformed gradients are used where solutions
Virology
American economist
contributed to the development of the binomial method for the valuation of options, the gradient method for asset allocation optimization, and returns-based
William_F._Sharpe
Type of feedforward neural network
Amari reported the first multilayered neural network trained by stochastic gradient descent, was able to classify non-linearily separable pattern classes.
Multilayer_perceptron
Type of artificial neural network
{E}}(n)={\frac {1}{2}}\sum _{{\text{output node }}j}e_{j}^{2}(n).} Using gradient descent, the change in each weight w i j {\displaystyle w_{ij}} is Δ w
Feedforward_neural_network
Tuning parameter (hyperparameter) in optimization
(machine learning) Hyperparameter optimization Stochastic gradient descent Variable metric methods Overfitting Backpropagation AutoML Model selection Self-tuning
Learning_rate
Signal processing computational method
the correct value of w {\displaystyle \mathbf {w} } , we can use gradient descent method. We first of all whiten the data, and transform x {\displaystyle
Independent component analysis
Independent_component_analysis
Metropolitan Reticular Matrix (1996) planning
method is an indicative approach to consensus building rather than a compulsory method. As such, it is more based in social capital continuous policy
Metropolitan Reticular Matrix Planning
Metropolitan_Reticular_Matrix_Planning
Method of a dimension reduction
rather than the mean. These properties allow use for explicit kernel methods, bilinear pooling in neural networks and is a cornerstone in many numerical
Count_sketch
Field of study in artificial intelligence
with the forget set. These approaches address a key weakness of gradient-based methods: apparent unlearning may reflect surface-level output suppression
Machine_unlearning
Smooth approximation of one-hot arg max
function itself) computationally expensive. What's more, the gradient descent backpropagation method for training such a neural network involves calculating
Softmax_function
Generative adversarial network variant
spectral normalization method. Instead of strictly bounding ‖ D ‖ L {\displaystyle \|D\|_{L}} , we can simply add a "gradient penalty" term for the discriminator
Wasserstein_GAN
traditionally used to tackle these problems: exact methods and metaheuristics.[disputed – discuss] Exact methods allow to find exact solutions but are often
Parallel_metaheuristic
Decentralized machine learning
total dataset and then used to make one step of the gradient descent.. Federated stochastic gradient descent is the analog of this algorithm to the federated
Federated_learning
Subfield of machine learning
successfully applied to few-shot image classification benchmarks and to policy-gradient-based reinforcement learning. Variational Bayes-Adaptive Deep RL (VariBAD)
Meta-learning (computer science)
Meta-learning_(computer_science)
Mathematical concept
logarithmic soft-max, making standard gradient-based optimization applicable. Unlike typical scalarization methods, it guarantees exploration of the entire
Multi-objective_optimization
Type of large language model
impact of large AI systems have led to calls for more efficient training methods and more transparency in reporting resource usage. Vision transformer Haddad
Generative pre-trained transformer
Generative_pre-trained_transformer
Type of aqueduct built in ancient Rome
along a slight overall downward gradient within conduits of stone, brick, concrete or lead; the steeper the gradient, the faster the flow. Most conduits
Roman_aqueduct
Machine learning methods using multiple input modalities
the tokenization method, to allow image inputs, and video inputs. GPT-4o can process and generate text, audio and images. A common method to create multimodal
Multimodal_learning
Way to sort sperm cells in fertilization
methods have been used to sort sperm before the advent of flow cytometry. Density gradient centrifugation (in a continuous or discontinuous gradient)
Sperm_sorting
Tree-based ensemble machine learning methods
learning method Decision tree learning – Machine learning algorithm Ensemble learning – Statistics and machine learning technique Gradient boosting –
Random_forest
Specialized notation for multivariable calculus
many derivatives in an organized way. As a first example, consider the gradient from vector calculus. For a scalar function of three independent variables
Matrix_calculus
American operations researcher and academic
co-developed the knowledge gradient method for sequential learning problems, in collaboration with Peter Frazier. The method has been the subject of multiple
Warren_B._Powell
with new arrivals to the system and otherwise is linear with negative gradient. By giving a relation for the distribution of unfinished work in terms
Beneš_method
Numerical method for solving boundary value problems
traditional methods struggle with stability or convergence. Mixed Finite Element Method: In mixed methods, additional variables (such as fluxes or gradients) are
Proper generalized decomposition
Proper_generalized_decomposition
Numerical method that reduces the complexity of computationally intensive simulations
The proper orthogonal decomposition is a numerical method that enables a reduction in the complexity of computer intensive simulations such as computational
Proper orthogonal decomposition
Proper_orthogonal_decomposition
Tasks in machine learning
using a supervised learning method, for example using optimization methods such as gradient descent or stochastic gradient descent. In practice, the training
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Type of database that uses vectors to represent other data
feature vectors may be computed from the raw data using machine learning methods such as feature extraction algorithms, word embeddings or deep learning
Vector_database
Intelligence of machines
engineering, mathematics and computer science that develops and studies methods and software that enable machines to perceive their environment and use
Artificial_intelligence
Computational model used in machine learning
the weights. The weight updates can be done via stochastic gradient descent or other methods, such as extreme learning machines, "no-prop" networks, training
Neural network (machine learning)
Neural_network_(machine_learning)
systems of linear equations Biconjugate gradient method: solves systems of linear equations Conjugate gradient: an algorithm for the numerical solution
List_of_algorithms
Method of measuring prediction error
Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other
Out-of-bag_error
3D reconstruction technique
between the predicted image and the original image can be minimized with gradient descent over multiple viewpoints, encouraging the MLP to develop a coherent
Neural_radiance_field
Statistical method
Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers
Random_sample_consensus
Algorithm for modelling sequential data
propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without
Transformer_(deep_learning)
Machine learning-powered structure design
optimal subgraph within a large graph. The controller is trained with policy gradient to select a subgraph that maximizes the validation set's expected reward
Neural_architecture_search
Use of complex numbers to evaluate integrals
integrand to 2 cos 6x − 4 cos 4x + 2 cos 2x and continue from there. Either method gives ∫ sin 2 x cos 4 x d x = − 1 24 sin 6 x + 1 8 sin 4 x − 1 8
Integration using Euler's formula
Integration_using_Euler's_formula
Pulse sequence used in medical imaging
(FLASH MRI) is a particular sequence of magnetic resonance imaging. It is a gradient echo sequence which combines a low-flip angle radio-frequency excitation
Fast low angle shot magnetic resonance imaging
Fast_low_angle_shot_magnetic_resonance_imaging
Type of machine learning model
the tokenization method, to allow image inputs, and video inputs. GPT-4o can process and generate text, audio and images. A common method to create multimodal
Large_language_model
POLICY GRADIENT-METHOD
POLICY GRADIENT-METHOD
Female
Russian
(Полина) Short form of Russian Apollinariya, POLINA means "of Apollo."
Boy/Male
British, English
Great
Male
Polish
Polish pet form of Czech/Polish Jakub, KUBA means "supplanter."
Girl/Female
Latin
Grace.
Surname or Lastname
Swedish
Swedish : unexplained.German : unexplained.English : unexplained.
Boy/Male
Czechoslovakian
Barber.
Girl/Female
Arabic, Muslim
Intelligent
Surname or Lastname
Catalan and Polish
Catalan and Polish : from a short form of the personal name Hipolit (see French Hypolite).English : variant of Pollitt.
Girl/Female
Australian, Finnish, Polish, Swedish
Bright; Shining; Radiant
Surname or Lastname
English (Dorset)
English (Dorset) : variant of Pouncey.
Girl/Female
Christian, Hindu, Indian
Happiness
Female
Polish
Polish-Jewish pet form of Polish Henrieta, YETTA means "little home-ruler."
Surname or Lastname
English (Essex)
English (Essex) : variant spelling of Polly.French : variant of Pollet.Altered spelling of French Polly.Variant spelling of Poley.
Girl/Female
Indian, Sindhi, Tamil
Beauty Personified; Bright; Brilliant
Male
French
French form of Roman Latin Gratian, GRATIEN means "pleasing, agreeable."
Boy/Male
German
People's Spirit
Girl/Female
Indian
Boy/Male
American, British, English
Gray-haired; Son of the Gray Family; Son of Gregory
Male
Polish
Polish form of Greek Methodios, METODY means "method."
Girl/Female
Hebrew American English
Wished-for child; rebellion; bitter.
POLICY GRADIENT-METHOD
POLICY GRADIENT-METHOD
Boy/Male
Arabic, Muslim
Honest Loving Blessings
Boy/Male
Hindu
Boy/Male
American, British, English
Severe; Strict
Girl/Female
Indian
Friend
Male
French
French form of German Filabert, FULBERT means "very bright."Â
Girl/Female
Indian
Like God
Boy/Male
Tamil
Jithendriyan | ஜீதேநà¯à®¤à¯à®°à®¿à®¯à®¨
The one who wins over senses
Girl/Female
Indian, Telugu
River Name
Girl/Female
German
Pure; Little and Womanly; Virgin; Female Version of Charles or Carl
Boy/Male
Hindu, Indian
Lord Hanuman; The Monkey God of Ramayana
POLICY GRADIENT-METHOD
POLICY GRADIENT-METHOD
POLICY GRADIENT-METHOD
POLICY GRADIENT-METHOD
POLICY GRADIENT-METHOD
v. t.
Hence, to refine; to wear off the rudeness, coarseness, or rusticity of; to make elegant and polite; as, to polish life or manners.
n.
Wrong policy; impolicy.
a.
Moving by steps; walking; as, gradient automata.
v. t.
To polish; to refine; to render polite.
imp. & p. p.
of Police
n.
A step or raised shelf, as above a sideboard or altar. Cf. Superaltar, and Gradin.
a.
Pertaining to, or troubled with, colic; as, a colicky disorder.
n.
Military police, the body of soldiers detailed to preserve civil order and attend to sanitary arrangements in a camp or garrison.
n.
Alt. of Gradine
n.
The quality of being impolitic; inexpedience; unsuitableness to the end proposed; bads policy; as, the impolicy of fraud.
n.
The rate of increase or decrease of a variable magnitude, or the curve which represents it; as, a thermometric gradient.
a.
Rising or descending by regular degrees of inclination; as, the gradient line of a railroad.
v. t.
To keep in order by police.
a.
Giving off rays; -- said of a bearing; as, the sun radiant; a crown radiant.
n.
Civil polity.
n.
A method of gambling by betting as to what numbers will be drawn in a lottery; as, to play policy.
n.
Policy; art; management.
v.
A disease of the hair (Plica polonica), in which it becomes twisted and matted together. The disease is of Polish origin, and is hence called also Polish plait.
v. t.
To make clean; as, to police a camp.
a.
Beaming with vivacity and happiness; as, a radiant face.