Andere Kunden interessierten sich auch für
![Constrained Reinforcement Learning with Average Reward Objective]( "Constrained Reinforcement Learning with Average Reward Objective")
Constrained Reinforcement Learning with Average Reward Objective75,99 €![Output Feedback Reinforcement Learning Control for Linear Systems]( "Output Feedback Reinforcement Learning Control for Linear Systems")
Output Feedback Reinforcement Learning Control for Linear Systems110,99 €![Output Feedback Reinforcement Learning Control for Linear Systems]( "Output Feedback Reinforcement Learning Control for Linear Systems")
Output Feedback Reinforcement Learning Control for Linear Systems110,99 €![Steuerung eines autonomen Fahrzeugs durch Deep Reinforcement Learning]( "Steuerung eines autonomen Fahrzeugs durch Deep Reinforcement Learning")
Steuerung eines autonomen Fahrzeugs durch Deep Reinforcement Learning44,99 €![Numerical Methods for Convex Multistage Stochastic Optimization]( "Numerical Methods for Convex Multistage Stochastic Optimization")
Numerical Methods for Convex Multistage Stochastic Optimization68,99 €![Learning Spaces]( "Learning Spaces")
Learning Spaces125,99 €![Economic Policy of the People's Republic of China]( "Economic Policy of the People's Republic of China")
Economic Policy of the People's Republic of China58,84 €
This monograph introduces various value-based approaches for solving the policy evaluation problem in the online reinforcement learning (RL) scenario, which aims to learn the value function associated with a specific policy under a single Markov decision process (MDP). Approaches vary depending on whether they are implemented in an on-policy or off-policy manner. In on-policy settings, where the evaluation of the policy is conducted using data generated from the same policy that is being assessed, classical techniques such as TD(0), TD(¿), and their extensions with function approximation or variance reduction are employed in this setting. For off-policy evaluation, where samples are collected under a different behavior policy, this monograph introduces gradient-based two-timescale algorithms like GTD2, TDC, and variance-reduced TDC. These algorithms are designed to minimize the mean-squared projected Bellman error (MSPBE) as the objective function. This monograph also discusses their finite-sample convergence upper bounds and sample complexity.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.