Policy gradient methods are a class of reinforcement learning algorithms. Policy gradient methods are a sub-class of policy optimization methods. Unlike...

policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method,...

reinforcement learning (RL) algorithms that combine policy-based RL algorithms such as policy gradient methods, and value-based RL algorithms such as value iteration...

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}}...

Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate...

methods. Gradient-based methods (policy gradient methods) start with a mapping from a finite-dimensional (parameter) space to the space of policies:...

contributions to the field, including temporal difference learning and policy gradient methods. Richard Sutton was born in either 1957 or 1958 in Ohio, and grew...

resulting algorithm is called gradient-boosted trees; it usually outperforms random forest. As with other boosting methods, a gradient-boosted trees model is...

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...

machine learning inspired by behaviorist psychology "REINFORCE", a policy gradient method (often used as PPO) Reinforcement theory in the field of communication...

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...

Interior-point methods (also referred to as barrier methods or IPMs) are algorithms for solving linear and non-linear convex optimization problems. IPMs...

Bedi; Csaba Szepesvari; Mengdi Wang (November 2020). "Variational Policy Gradient Method for Reinforcement Learning with General Utilities" (PDF). Advances...

Policy gradient methods directly optimize the agent’s policy by adjusting parameters in the direction that increases expected rewards. These methods are...

In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered...

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...

Most recent systems use policy-gradient methods such as Proximal Policy Optimization (PPO) because PPO constrains each policy update with a clipped objective...

employed classical gradient-based methods to structural optimization problems. The method of usable feasible directions, Rosen's gradient projection (generalized...

Polyak, subgradient–projection methods are similar to conjugate–gradient methods. Bundle method of descent: An iterative method for small–medium-sized problems...

advantageous to train (parts of) an LSTM by neuroevolution or by policy gradient methods, especially when there is no "teacher" (that is, training labels)...

power from salinity gradient. One method to utilize salinity gradient energy is called pressure-retarded osmosis. In this method, seawater is pumped into...

In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is...

Kaiqing; Jovanovic, Mihailo; Basar, Tamer (2020). Natural policy gradient primal-dual method for constrained Markov decision processes. Advances in Neural...

(bagging) Cascading CoBoosting Logistic regression Maximum entropy methods Gradient boosting Margin classifiers Cross-validation List of datasets for machine...

The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational...

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...

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...

This form has applications in Stein variational gradient descent and Stein variational policy gradient. The univariate probability density function for...

Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), Asynchronous Advantage Actor-Critic (A3C), Deep Deterministic Policy Gradient (DDPG)...

due to high variance. Some reinforcement learning methods, e.g. DDPG (Deep Deterministic Policy Gradient), are more sensitive to hyperparameter choices than...