Incremental natural actor-critic algorithms - Bhatnagar, Sutton, et al. - 2008 () Citation Context ...behavior will be invariant. Technically, the property that must be verified is that J(g · y, u) = J(y, u), for all g ∈ G Y . (5)6 An example in the literature is in “...
然而,现有方法,如基于Actor-Critic结构和经验回放(ER)的方法,面临着分布偏移、低效率和知识共享能力有限等问题。 论文的主要贡献如下: 1. 提出了使用Decision Transformer(DT)作为更适合的离线连续学习者,以解决这些问题。DT在离线强化学习中表现出极高的学习效率,并能够忽略分布偏移问题。 2. 引入了多头DT(MH-DT)...
Natural actor criticIncremental learningImplicit updateNatural policy gradient (NPG) methods are promising approaches to finding locally optimal policy parameters. The NPG approach works well in optimizing complex policies with high-dimensional parameters, and the effectiveness of NPG methods has been ...
Natural policy gradientIncre-mental natural actor criticIncremental learningImplicit updateThe natural policy gradient (NPG) method is a promising approach to find a locally optimal policy parameter. The NPG method has been demonstrated remarkable successes in many fields, including the large scale ...
algorithm. The Critic estimates the value-function according to the iLSTD(λ) algorithm, and the Actor updates the policy parameter based on a regular gradient. Simulation results concerning a grid world with 10×10 size illustrate that the AC algorithm based on iLSTD(λ) not only has quick ...
The improvement in learning evaluation efficiency of the Critic will contribute to the improvement in policy learning performance of the Actor. Simulation results on the learning control of an inverted pendulum and a mountain-car problem illustrate the effectiveness of the two proposed AC algorithms in...
Soft actor-critic algorithms and applications. arXiv 2018, arXiv:1812.05905. [Google Scholar] Fan, J.; Ou, Y.; Wang, P.; Xu, L.; Li, Z.; Zhu, H.; Zhou, Z. Markov decision process of optimal energy management for plug-in hybrid electric vehicle and its solution via policy ...