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...
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...
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。 总结:感觉有点大杂烩...
那些回答说：“因为使用replay buffer所以就是off-policy“的同学逻辑反了，是因为算法本身是off-policy，...
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。
Off-Policy Actor-Critic with Emphatic Weightings This paper proposes a gradient-based multi-agent actor-critic algorithm for off-policy reinforcement learning using importance sampling. Our algorithm is i... E Graves,E Imani,R Kumaraswamy,... - 《Journal of Machine Learning Research》 被引量: ...
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...
4. From Soft Policy Iteration to Soft Actor-Critic 可以从策略迭代方法的最大熵变体开始,得出我们的异策SAC算法。我们将首先介绍该推导,验证相应算法从其密度类别收敛到最优策略,然后根据此理论提出一种实用的深度RL算法。 4.1. Derivation of Soft Policy Iteration ...
In this paper, we rethink off-policy learning via Coordinate Ascent Policy Optimization (CAPO), an off-policy actor-critic algorithm that decouples policy improvement from the state distribution of the behavior policy without using the policy gradient. This design obviates the need for distribution ...
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...