Titlebook: Recent Advances in Reinforcement Learning; Leslie Pack Kaelbling Book 1996 Springer Science+Business Media New York 1996 Performance.algor

怕失去錢

Editorial,for the journal. One measure of our success is that for 1994 in the category of “Computer Science/Artificial Intelligence,” . was ranked seventh in citation impact (out of a total of 32 journals) by the Institute for Scientific Information. This reflects the many excellent papers that have been subm

Grating

Introduction, reinforcement learning into a major component of the machine learning field. Since then, the area has expanded further, accounting for a significant proportion of the papers at the annual . and attracting many new researchers.

流逝

Efficient Reinforcement Learning through Symbiotic Evolution,ough genetic algorithms to form a neural network capable of performing a task. Symbiotic evolution promotes both cooperation and specialization, which results in a fast, efficient genetic search and discourages convergence to suboptimal solutions. In the inverted pendulum problem, SANE formed effect

MAIM

ingrate

Feature-Based Methods for Large Scale Dynamic Programming,ve large scale stochastic control problems. In particular, we develop algorithms that employ two types of feature-based compact representations; that is, representations that involve feature extraction and a relatively simple approximation architecture. We prove the convergence of these algorithms a

向外供接觸

On the Worst-Case Analysis of Temporal-Difference Learning Algorithms, takes place in a sequence of trials, and the goal of the learning algorithm is to estimate a discounted sum of all the reinforcements that will be received in the future. In this setting, we are able to prove general upper bounds on the performance of a slightly modified version of Sutton’s so-call

地名詞典

NIL

Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results,cal tasks than the much better studied discounted framework. A wide spectrum of average reward algorithms are described, ranging from synchronous dynamic programming methods to several (provably convergent) asynchronous algorithms from optimal control and learning automata. A general sensitive disco

不規(guī)則

AVERT