Actor-critic (AC)DiscrepancyVarianceImportance sampling (IS)Off-policyRelative importance sampling (RIS)Off-policy learning exhibits greater instability when compared to on-policy learning in reinforcement learning (RL). The difference in probability distribution between the target policy () and the ...
提出第一个离线的演员评论家算法,名字叫做Off-PAC算法,是Off-Policy Actor-Critic的缩写。 提供了离线策略梯度理论以及Off-PAC的收敛证明。 提供了一组实验上的比较,在三个标准离线问题上展示了Off-PAC超越了其他的算法 算法推导 这篇文章的值函数: 它和我们常见的带有discount的值函数并不相同,不过也用了类似的想...
This paper presents the first actor-critic algorithm for off-policy reinforcement learning. Our algorithm is online and incremental, and its per-time-step complexity scales linearly with the number of learned weights. Previous work on actor-critic algorithms is limited to the on-policy setting and...
即value-based方法，在Policy-gradient任务中这一类都是叫做actor-critic的方案。其中critic就是评估者，对...
先是分析了把actor-critic变成off-policy的过程中需要做的修正,主要是importance sampling和V-trace,以及即使这样也会产生误差。然后就说把off-policy的数据混合on-policy的数据一起训练缓解节这个问题,并在此基础上还加了个trust region的限制。最后混在一起成了个off-policy actor-critic方法。
先是分析了把actor-critic变成off-policy的过程中需要做的修正,主要是importance sampling和V-trace,以及即使这样也会产生误差。然后就说把off-policy的数据混合on-policy的数据一起训练会环节这个问题,并在此基础上还加了个trust region的限制。最后混在一起成了个off-policy actor-critic方法。
Q-learning每次只需要执行一步动作得到(s,a,r,s’)就可以更新一次;由于a’永远是最优的那个action,因此估计的策略应该也是最优的,而生成样本时用的策略(在状态s选择的a)则不一定是最优的(可能是随机选择),因此是off-policy。基于experience replay的方法基本上都是off-policy的。 sarsa必须执行两次动作得到(s...
an off-policy iteration algorithm is designed to iteratively improve the target policy, and the convergence of the algorithm is proved theoretically. Actor-critic neural networks along with the gradient descent approach are employed to approximate optimal control policies and performance index functions us...
Two numerical examples serve as a demonstration of the off-policy algorithm performance. 展开 关键词: Actor-critic adaptive self-organizing map (SOM multiple-model off-policy reinforcement learning (RL optimal control DOI: 10.1109/TCYB.2016.2618926 被引量: 1 ...
文章要点:这篇文章提出一个新的experience replay的方法,improved SAC (ISAC)。大概思路是先将replay buffer里面好的experience单独拿出来作为好的experience。然后再混合当前最新收集的样本一起用来更新,就相当于好的off-policy data混合最新的on-policy data。