Multitask model-free reinforcement learning Andrew Saxe Stanford University, Stanford, CA, USA Abstract: Conventional model-free reinforcement learning algorithms are limited to performing only one task, such as navi- gating to a single goal location in a maze, or reaching one goal state in the ...
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...
两篇论文传送门: 2018年1月挂arXiv,8月被ICML收录,Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 2018年12月挂arXiv,Soft Actor-Critic Algorithms and Applications 2.5 DPG Deterministic Policy Gradient 是确定性策略梯度方法,是off-policy、连续状态、连续动...
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)...
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...
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 ...
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...
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-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...
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...