Constrained Policy Optimization(CPO)[1]是解决CMDP的一个经典算法。通过local policy search + trust region recovery的方法将单步策略更新限定在不违反约束的增长方向上。原理和NPG[2]或TRPO[3]基本相同,区别在于引入了环境约束: 实际操作时候也是进行了一阶和二阶近似: 在只有一个约束时,该问题是有闭式解的(多...
作者:凯鲁嘎吉 - 博客园http://www.cnblogs.com/kailugaji/ 这篇文章详细讲解Constrained Policy Optimization (CPO)的公式推导,文献来自于:Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel. Constrained Policy Optimization. Proceedings of the 34th International Conference on Machine Learning, PMLR 70:22...
上面的推论跟 TRPO 论文中的下界的推导类似,因此,可以按照 TRPO 的推导方式得到 CPO 的最终代理优化目标。 Surrogate Optimization Function 对于目标函数来说,我们只需要推高公式(7)的下界就行了,对于策略的约束来说,只需要保证新策略依然满足约束条件就行了,所以把公式(7)(8)带进公式(6)有: \begin{aligned}\...
git submodule add -f https://github.com/jachiam/cpo sandbox/cpo Run CPO in the Point-Gather environment with python sandbox/cpo/experiments/CPO_point_gather.py Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel. "Constrained Policy Optimization".Proceedings of the 34th International Conference...
Constrained policy optimizationA multi-objective optimization model for ship path planning is constructed.CPO is introduced to solve multi-objective path planning for ships.Unique reward functions are designed in the CPO framework to improve performance.CPO is validated in four environments, showing ...
2. Constrained Variational Policy Optimization (CVPO) 主要介绍论文的核心内容,包括Constrained Markov Decision Processes、Primal-Dual View vs Inference View、Constrained RL as Inference、Constrained E-step——固定θ,求q、M-step——固定q,求θ、以及算法流程。
之前已经简单的说明了 CPO 论文里的大致框架,最近又回顾了一下 CPO 中的证明过程,本文用于总结文中上下界的证明. 自认为应该是讲的比较清楚的,只要把预备知识里的东西搞懂了,后面的推导就比较顺其自然了. SHELCLin:Constrained Policy Optimization63 赞同 · 8 评论文章 ...
Recent advances in policy search algorithms (Mnih et al., 2016; Schulman et al., 2015; Lillicrap et al., 2016; Levine et al., 2016) have enabled new capabilities in high-dimensional control, but do not consider the constrained setting. We propose Constrained Policy Optimization (CPO), the...
Compared with Deep Q-network (DQN) and Constrained Policy Optimization (CPO), the search efficiency of the algorithm proposed in this paper is improved by 40% and 12%, respectively. Moreover, it achieved a 91.3% success rate of collision avoidance during training. The methodology could also ...
Constrained Proximal Policy Optimization The problem of constrained reinforcement learning (CRL) holds significant importance as it provides a framework for addressing critical safety satisfaction concerns in the field of reinforcement learning (RL). However, with the introduction of constraint satisfaction, ...