算法步骤 Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor使用一个策略π网络,两个Q网络,两个V网络(其中一个是Target V网络),关于这篇文章的介绍可以参考强化学习之图解SAC算法 Soft Actor-Critic Algorithms and Applications使用 一个策略π网络,四个Q网络(其中...
本文提出了soft actor-critic算法,一个基于最大熵强化学习框架的off-policy actor-critic DRL算法,在这个框架中,actor旨在最大化期望收益同时也最大化熵,也就是,也就是说,在完成任务的同时尽可能随机地行动,先前基于此框架的深度RL方法被定义为Q-learning方法,通过将off-policy更新与稳定的随机行动者-评论家公式相...
在这种情况下,将策略称为actor,将价值函数称为critic。许多actor-critic算法都建立在标准同策策略梯度公式基础上,以更新actor(Peters&Schaal, 2008),其中许多工作还考虑了策略的熵,但是他们没有使用它来最大化熵,而是使用它作为正则化器(Schulman et al., 2017b; 2015; Mnih et al., 2016; Gruslys et al., ...
第二篇:《Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor》 这篇论文在第一篇的基础上,找到了优化策略网络的新方法(重参数化技巧)。然后给出了新的网络结构。同时作者开始吸收DDPG和TD3的优势。 第三篇:《Soft Actor-Critic Algorithms and Applications》 这...
SAC是基于最大熵(maximumentropy)这一思想发展的RL算法,其采用与PPO类似的随机分布式策略函数(StochasticPolicy),并且是一个off-policy,actor-critic算法,与其他RL算法最为不同的地方在于,SAC在优化策略以获取更高累计收益的同时,也会最大化策略的熵。SAC在各种常用的benchmark以及真实的机器人控制任务中性能优秀,而且...
通过Soft Q-Learning的概念，SAC将最大熵与Soft Q-function相结合，定义了Energy-Based Policy，实现了策略与最大熵目标之间的紧密联系。这一创新使得SAC在最大化熵的同时，能够收敛于最优策略。在Soft Actor-Critic算法的具体实现中，包括了神经网络化表示、更新公式设计以及自动调整温度参数的机制。算法...
In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy. That is, to succeed at the task while acting as...
In adherence to the actor-critic formulation, SAC performs policy iteration by cycling between policy evaluation, which calculates the value function 𝑣𝜋 of a policy 𝜋, and policy improvement, where the value function is used to improve the policy [13]. Policy iteration for SAC has a ...
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 10–15 July 2018; pp. 1861–1870. [Google Scholar] Chen, W.T.; Zhu, A.Y.; Sanjeev, V.; Khorasani...
Soft Actor Critic (SAC) is an algorithm that optimizes a stochastic policy in an off-policy way, forming a bridge between stochastic policy optimization and DDPG-style approaches. It isn’t a direct successor to TD3 (having been published roughly concurrently), but it incorporates the clipped ...