2018-Simple random search provides a competitive approach to reinforcement learning random search比很多牛逼的RL方法效果好(在MuJoCo上,环境较简单) 1994-Asynchronous Stochastic Approximation and Q-Learning Q-learning收敛性与exploration策略无关 2017-Bridging the gap between value and policy RL 2017-Equivalenc...
1. What is reinforcement learning? 2. How does RL differ from other ML paradigms? 3. What are agents and how do agents learn? 4. What is the difference between a policy function and a value function? 5. What is the difference between model-based and model-free learning? 6. What are ...
我最开始学习是从Playing Atari with Deep Reinforcement Learning和Simple Reinforcement Learning with Tensorflow开始的,论文主要是讲DQN的 论文中有对MDR&BellmanEquation的详细描述, 简单抽离一下: 我们的agent在每一个场景下可以做出一系列的action中的一个(A = {1, . . . , K}),因为这个action会获得相应rewa...
Sutton早在1999年就发表论文Policy Gradient Methods for Reinforcement Learning with Function Approximation证明了随机策略梯度的计算公式: 证明过程就不贴了,有兴趣读一下能加深下理解。也可以读读 REINFORCE算法(with or without Baseline)Simple statistical gradient-following algorithms for connectionist reinforcement le...
David Silver ICML2016 Tutorial: Deep Reinforcement Learning 中文讲稿 [https://mp.weixin.qq.com/s/sq5_ZBoWpp9JOPaGkycKyg] DQN tutorial [https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-4-deep-q-networks-and-beyond-8438a3e2b8df#.28wv34w3a] ...
#Reinforcement learning approach#Actions in discrete time: Solution strategy#Markov Decision Process#Policy#Value Functions#Bellman Equation#Q-learning Algorithm#Example 1: A robot explores a room with unknown obstacles with Q-learning algorithm#OpenAI Gym#Define utility functions#A simple Q-learning ...
MachineLearning TomM.Mitchell outline WhatisReinforcementLearning? MethodsUsedinReinforcementLearning TemporalDifferenceMethods Applications Introduction What’sreinforcementlearning? History Whatreinforcementlearningcando? ReinforcementLearning’sElement What’sreinforcementLearning? Reinforcementlearningaddressthequestionof how...
(OSU) 讲座题目:Reward-free RL via Sample-Efficient Representation Learning 讲座摘要:As reward-free reinforcement learning (RL) becomes a powerful framework for a variety of multi-objective applications, representation learning arises as an effective technique to deal with the curse of dimensionality in...
DRO,直接奖励优化,参阅论文《Offline regularised reinforcement learning for large language models alignment》。 融合SFT 和对齐 之前的研究主要还是按顺序执行 SFT 和对齐,但事实证明这种方法很费力,并会导致灾难性遗忘。后续的研究有两个方向:一是将...
In recent years, Reinforcement Learning (RL), has become a popular field of study as well as a tool for enterprises working on cutting-edge artificial intelligence research. To this end, many researchers have built RL frameworks such as openAI Gym and KerasRL for ease of use. While these wo...