Model-free即没有对环境的知识,不对环境建模,与model-based相对。 model-free部分的算法大致可以分为三个部分:policy optimization,Q-learning以及两者的结合。 几个基本的Model-free算法分类 [论文]Model-free 论文整理 Playing Atari with Deep Reinforcement Learning NIPS Deep Learning Workshop 2013 |paper Volodym...
Model-free即没有对环境的知识,不对环境建模,与model-based相对。 model-free部分的算法大致可以分为三个部分:policy optimization,Q-learning以及两者的结合。 几个基本的Model-free算法分类 [论文]Model-free 论文整理 Playing Atari with Deep Reinforcement Learning NIPS Deep Learning Workshop 2013 |paper Volodym...
While the concept of model-free reinforcement learning demonstrates various advantages over existing strategies, the literature relies heavily on value-based methods that can hardly handle complex HVAC systems. This paper conducts experiments to evaluate four actor-critic algorithms in a simulated data ...
reinforcement learning algorithms are developed based on Bellman optimality principle (Bellman, 1952), such as the on-policy IRL method (Vrabie & Lewis, 2009; Xu, Pan, & Shen, 2021), the off-policy IRL method (Jiang & Jiang, 2012; Luo, Wu, Huang, & Liu, 2014), and the Q-learning ...
在这种情况下,选择有效的 model-free algorithms 使用更加合适的,特定任务的表示,以及 model-based algorithms 来用监督学习的方法来学习系统的模型,并且在该模型下进行策略的优化。利用特定任务的表示显著的改善了效率,但是限制了能够从更加广泛的 domain 知识上学习和掌握的任务的范围。利用 model-based RL 能够改善...
IV. ALGORITHMS 我们的框架中应用了三种最先进的无模型深度强化学习算法来学习驾驶策略。我们将在本章中简要介绍它们。 A. Double Deep Q-Network (DDQN) B. Twin Delayed Deep Deterministic Policy Gradient (TD3) C. Soft Actor Critic (SAC) V. EXPERIMENTS ...
Recent model-free reinforcement learning algorithms have proposed incorporating learned dynamics models as a source of additional data with the intention of reducing sample complexity. Such methods hold the promise of incorporating imagined data coupled with a notion of model uncertainty to accelerate the...
we propose parallel reinforcement-learning models of card sorting performance, which assume that card sorting performance can be conceptualized as resulting from model-free reinforcement learning at the level of responses that occurs in parallel with model-based reinforcement learning at the categorical lev...
model-based algorithms generally retain some transi- tion information during learning whereas model-free algorithms only keep value-function information. In- stead of formalizing this intuition, we have decided to PAC Model-Free Reinforcement Learning adopt a crisp, if somewhat unintuitive, definition...
Reinforcement learning methods are often considered as a potential solution to enable a robot to adapt to changes in real time to an unpredictable environment. However, with continuous action, only a few existing algorithms are practical for real-time learning. In such a setting, most effective me...