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.
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 ...
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...
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...
Arguably, this is not the most efficient way to find an optimal policy and in fact, several methods exist for combining model-free reinforcement learning with inverse reinforcement learning (IRL) algorithms, which are used to infer a reward function given state-action pairs sampled from an optimal...
model-free (MF) reinforcement learning algorithms with replays (i.e., either reactivations of episodic memory buffer during learning phase for MF algorithms, or mental simulations of (state,action,new_state,reward) quadruplet events with the internal model during inference phase for MB algorithms)...
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 ...
3. Model-free RL Put simply, model-free algorithms refine their policy based on the consequences of their actions. Let’s explore it with an example! Consider this environment: In this example, we want the agent (in green) to avoid the red squares and reach the blue one in as few step...
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...