政策梯度方法直接参数化策略，通过梯度优化寻找最优策略参数。其优点是直接与回报关联，缺点是梯度估计的高方差问题。softmax和高斯策略分别适用于离散和连续的行动空间。Actor-Critic方法结合了基于TD的思想，允许在线更新策略，与基于Monte Carlo的REINFORCE方法形成对比。REINFORCE方法存在高方差问题，而Actor-Cr...
Policy Gradient分两大类:基于Monte-Carlo的REINFORCE(MC PG)和基于TD的Actor Critic(TD PG)。 REINFORCE是Monte-Carlo式的探索更新,也就是回合制的更新,至少要等一个回合结束才能更新policy; 而Actor Critic是基于TD的,也就是说可以按step来更新,不需要等到回合结束,是一种online learning。 蒙特卡洛存在较高的vari...
1.1 actor-critic回顾 回顾上一讲提到的actor-critic算法,算法流程如下图所示。大致原理是:首先,使用agent从环境采集很多轨迹;接着,使用Monte Carlo方法或bootstrapping方法拟合state函数V¯πθ(s);然后,计算这些轨迹的Advantage值,并计算policy gradient;最后,更新网络参数。actor-critic也可以用右图来描述:橙色方框...
对于Actor-Critic算法,说法错误的是A.Actor-Critic算法结合了policy-based和value-based的方法B.Critic网络是用来输出动
We introduce Quality-Diversity Actor-Critic (QDAC), an off-policy actor-critic deep reinforcement learning algorithm that leverages a value function critic and a successor features critic to learn high-performing and diverse behaviors. In this framework, the actor optimizes an objective that ...
We employ the return distribution function within the maximum entropy RL framework in order to develop what we call the Distributional Soft Actor-Critic (DSAC) algorithm, which is an off-policy method for continuous control setting. Unlike traditional distributional RL algorithms which typically only ...
Finally, the Proximal Policy Optimization (PPO) method was introduced into a multi-crawler simulation environment. This implementation of PPO combines clipped surrogate actor loss, critic loss and entropy loss. Generalized Advantage Estimate (GAE) was introduced to stabalize training by balancing bias ...