Policy Gradient

	Policy Gradient Policy gradient methods are a class of reinforcement learning algorithms. Policy gradient methods are a sub-class of policy optimization methods. Unlike value-based methods which learn a value function to derive a policy, policy optimization methods directly learn a policy function \pi that selects actions without consulting a value function. For policy gradient to apply, the policy function \pi_\theta is parameterized by a differentiable parameter \theta. Overview In policy-based RL, the actor is a parameterized policy function \pi_\theta, where \theta are the parameters of the actor. The actor takes as argument the state of the environment s and produces a probability distribution \pi_\theta(\cdot \mid s). If the action space is discrete, then \sum_ \pi_\theta(a \mid s) = 1. If the action space is continuous, then \int_ \pi_\theta(a \mid s) \mathrma = 1. The goal of policy optimization is to find some \theta that maximizes the expected episodic reward J(\theta):J(\theta) = ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Reinforcement Learning Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dyn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Quadratic Programming Quadratic programming (QP) is the process of solving certain mathematical optimization problems involving quadratic functions. Specifically, one seeks to optimize (minimize or maximize) a multivariate quadratic function subject to linear constraints on the variables. Quadratic programming is a type of nonlinear programming. "Programming" in this context refers to a formal procedure for solving mathematical problems. This usage dates to the 1940s and is not specifically tied to the more recent notion of "computer programming." To avoid confusion, some practitioners prefer the term "optimization" — e.g., "quadratic optimization." Problem formulation The quadratic programming problem with variables and constraints can be formulated as follows. Given: * a real-valued, -dimensional vector , * an -dimensional real symmetric matrix , * an -dimensional real matrix , and * an -dimensional real vector , the objective of quadratic programming is to find an -dimensional vector , that ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Deep Reinforcement Learning {{Short description, Subfield of machine learning Deep reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves training agents to make decisions by interacting with an environment to maximize cumulative rewards, while using deep neural networks to represent policies, value functions, or environment models. This integration enables DRL systems to process high-dimensional inputs, such as images or continuous control signals, making the approach effective for solving complex tasks. Since the introduction of the deep Q-network (DQN) in 2015, DRL has achieved significant successes across domains including games, robotics, and autonomous systems, and is increasingly applied in areas such as healthcare, finance, and autonomous vehicles. Deep reinforcement learning Introduction Deep reinforcement learning (DRL) is part of machine learning, which combines reinforcement learning (RL) and deep ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Reinforcement Learning Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dyn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]