DQfD的整个算法结构是DQN + Double learning + Dueling DQN + PER + n-step,前边四项的组合在文中又称PDD DQN。 训练总共分为两个阶段: 预训练阶段,目的是学习一个模仿示范者(专家)的、满足Bellman equation的值函数,从而使当智能体与环境进行真正交互时可以使用TD进行更新; 在线学习阶段,混合使用新...
本文介绍的是Deep Q-learning from Demonstrations (DQfD)算法，它由DeepMind团队提出。DQfD旨在利用少量示范加速学习过程，并结合优先回放机制自动评估示范数据的重要性。此算法结合了时序差分和对示范动作的监督学习分类，旨在解决强化学习中数据效率问题。本文的基准是Prioritized Dueling Double Deep Q-Networks...
假设条件:可以事先获得一堆的Demonstrations,知道reward function, 主要想法:利用Demonstrations 来per-training NN的weights来缓解cold start 解决方案:利用reward function标记Demonstrations,构造NN的loss function为:RL的loss + Demonstrations的loss, 来初始化NN的Q function,然后在training时候采用同样的loss function来衔接...
Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prio...
An implement of DQfD(Deep Q-learning from Demonstrations) raised by DeepMind:Learning from Demonstrations for Real World Reinforcement Learning - go2sea/DQfD
E. Learning from Demonstrations 从演示中学习(LfD)被人类用来在专家到学习者的知识传递过程中获得新技能。LfD对于奖励信号太稀疏或输入域太大而无法覆盖的初始探索很重要。在LfD中,智能体从演示中学习执行任务,通常以状态-动作对的形式,由专家提供,没有任何反馈奖励。然而,高质量和多样化的演示很难收集,导致学习次...
2) Fine-tune on a small dataset of clean expert demonstrations (190 thousand frames or 3 hours). This scale is an order of magnitude larger than prior work on imitation learning in FPS games, whilst being far more data efficient than pure RL algorithms. Video introduction: https://youtu....
For learning in simulated environments, it is fine, but when we make our agent learn in a real-world environment it causes a lot of problems. To overcome this, a researcher from Google's DeepMind introduced an improvement on DQN called deep Q learning from demonstrations (DQfd). If ...
DeepMind的一群大佬在本文提出一个叫做 Deep Q-learning from Demonstrations (DQfD) 的算法,意图用少量的示范,极大地加速学习过程,并且利用 prioritized replay mechanism (优先回放机制,一种DQN在采样上的改进方案)来自动评估示范数据的重要性。 DQfD 的工作原理是将时序差分与对于示范动作的监督学习分类结合在一起。
Pre-training: Agent与environment交互前的预训练,即引入模仿学习imitation learning的思想,对已有的Demonstrations做imitate,在online-training process 前,将不是单独去学习action,而是去学对应的Q function。 Supervised loss:利用监督学习的特性,从Demonstrations抽样来对于神经网络的Q function随机梯度下降计算 ...