策略改进(actor更新): 最大化\mathbb{E}_{s \sim \mathcal{B}, a \sim \pi{\phi}}[Q_{\theta}(s, a) - \alpha \log \pi_{\phi}(a|s)]. IV. DSAC WITH THREE REFINEMENTS (DSAC-T) A. 期望值替代 标准DSAC算法中,critic更新梯度包含两部分: 均值相关梯度:-\frac{y_z - Q_{\theta...
我们的工作聚焦于强化学习算法策略性能的提升,开发了完整的值分布策略迭代框架,基于值分布强化学习DSAC算法基础,借助三项改进工作: 1)引进Expected value substituting提高了学习过程的稳定性和效率; 2)引进Variance-based critic gradient adjusting减少了对人工调参的需要,使算法更具通用性; 3)引进Twin value distributio...
Distributional Soft Actor Critic for Risk Sensitive Learning Most of reinforcement learning (RL) algorithms aim at maximizing the expectation of accumulated discounted returns. Since the accumulated discounted return is a random variable, its distribution includes more information than its expecta... X Ma...
In this paper, we introduce the minimax formulation and distributional framework to improve the generalization ability of RL algorithms and develop the Minimax Distributional Soft Actor-Critic (Minimax DSAC) algorithm. Minimax formulation aims to seek optimal policy considering the most serious ...
Distributional Soft Actor-Critic with Three Refinements (DSAC-T) Requires Windows 7 or greater or Linux. Python 3.8. The installation path must be in English. Installation # Please make sure not to include Chinese characters in the installation path, as it may result in a failed execution. #...
DSAC; Distributional Soft Actor-Critic. Contribute to Jingliang-Duan/DSAC-v1 development by creating an account on GitHub.
智能驾驶课题组(iDLab)提出一种可减少过估计的Distributional Soft Actor-Critic(DSAC)算法,通过学习连续状态-动作回报分布(state-action return distribution)来动态调节Q值的更新过程,并证明引入该分布降低过估计的原理。本文基于异步并行计算框架PABAL来实施DSAC算法,Mujoco环境的验证表明:相比于目前最流行的强化学习算法...
Distributional Soft Actor-Critic with Three Refinements (DSAC-T) Requires Windows 7 or greater or Linux. Python 3.8. The installation path must be in English. Installation #Please make sure not to include Chinese characters in the installation path, as it may result in a failed execution.#clon...