如果还是不明白的话,可以看书里的一个例子: An incremental every-visit MC policy-evaluation algorithm 这个例子里面的行为策略和目标策略一目了然——用来产生观测数据的是行为策略,最终需要学习的、正在优化的策略是目标策略。我们依赖正在优化的策略产生数据进行迭代,就是同策略,否则就是异策略。这决定了算法是否能...
解释一下,Q-learning 是一个经典的Off-policy algorithm,所有的data都可以来自任何的策略,可以用以前的、别人的经验来进行学习。更新的值网络和产生数据的值网络不是一个。 而SASAR是一个经典On-policy algorithm。SASAR其实是{s,a,s',a,r}的缩写,表达了一个策略走两步更新一次。其与Q-learning最大的区别在...
In this article, we present a novel off-policy reinforcement learning (RL) algorithm called conservative distributional maximum a posteriori policy optimization (CDMPO). At first, to accurately judge whether the current situation satisfies the constraints, CDMPO adapts distributional RL method to ...
一种结合 off-policy 数据来训 RL 的方法。 具体可以看这篇博客,比本博客写得好(惭愧) 0 abstract In this work, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines, while also being able to leverage off-policy data....
It is the first-time application of the off-policy RL algorithm on this robust two-player zero-sum differential game problem. Additionally, the final algorithm’s convergence is demonstrated, and a simulation example is run to confirm its efficacy....
modify params in arguments.py, choose env, RL-algorithm, use PER and HER or not, gpu-id, and so on. run with train_tf.py or train_torch.py plot results:https://blog.csdn.net/hehedadaq/article/details/114044217 超强版强化学习画图脚本!
在强化学习中,行为策略和目标策略的区别在于,行为策略是智能体在环境中实际采取的策略,而目标策略是智能体希望学习的最优策略。¹ 行为策略和目标策略的差异会影响到强化学习算法的选择和性能。¹ 行为策略和目标策略都是强化学习中的重要概念。 (1) 强化学习中,确定性策略和随机策略的区别,以及各自经典的算法是...
An online synchronous approximate optimal learning algorithm for solving a multiplayer nonzero-sum game with unknown dynamical systems was developed in [4]. At present, off-policy can be an adaptive learning method that avoids identifying system dynamics for nonzero-sum games. The off-policy RL ...
In this section, we present an SPU-based off-policy RL algorithm to learn the solution of GARE (4) without knowing the system dynamics information. Assume that ut and vt are the behavior policies that are implemented in system (1) to generate data. On the contrary, uit=−Lixt, vit=...
This chapter deals with the optimal synchronization control problem for CT multi-agent systems based on graphical games and the cooperative optimal control problem for DT multi-player systems based on nonzero-sum games. First, we developed an off-policy RL algorithm to solve optimal synchronization ...