Deep RL Bootcamp Lecture 2: Sampling-based Approximations and Function Fitting

https://statweb.stanford.edu/~owen/mc/Ch-var-is.pdf https://zhuanlan.zhihu.com/p/29934206 blue curve is the lower bounded one conjugate gradient to solve the optimization problem. Fisher information matrix, natural policy gradient To write down an op

^ is the square root of epsilon a simplified version of hard version a more smooth way to find correct solution the first term is the REINFORCE term, and the seconde term is our grad log probability of our loss b is a stochastic node        more form

high bias if the robot has learnt something (no changes appear with iterations) however, in the real world tasks, the task could change a little bit, then the robot will failed to generalize. no matter how well we train the robot in situations, there

model free: high variance. model based: high bias within 1h of human demonstration of each task, VR!!! 原文地址:https://www.cnblogs.com/ecoflex/p/8990885.html

Tutorials on Inverse Reinforcement Learning 2018-07-22 21:44:39 1. Papers:  Inverse Reinforcement Learning: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.394.2178&rep=rep1&type=pdf Cooperative Inverse Reinforcement Learning: http://pape

近期深度强化学习领域日新月异,其中最酷的一件事情莫过于 OpenAI 和 DeepMind 训练智能体接收人类的反馈而不是传统的奖励信号.本文作者认为复现论文是提升机器学习技能的最好方式之一,所以选择了 OpenAI 论文<Deep Reinforcement Learning from Human Preferences>作为 target,虽获得最后成功,却未实现初衷.如果你也打算复现强化学习论文,那么本文经验也许是你想要的.此外,本文虽对强化学习模型的训练提供了宝贵经验,同时也映射出另外

Deep Reinforcement Learning for Visual Object Tracking in Videos 论文笔记 arXiv 摘要:本文提出了一种 DRL 算法进行单目标跟踪,算是单目标跟踪中比较早的应用强化学习算法的一个工作.  在基于深度学习的方法中,想学习一个较好的 robust spatial and temporal representation for continuous video data 是非常困难的.  尽管最近的 CNN based tracke

Deep Reinforcement Learning Papers A list of recent papers regarding deep reinforcement learning. The papers are organized based on manually-defined bookmarks. They are sorted by time to see the recent papers first. Any suggestions and pull requests

Collaborative Deep Reinforcement Learning for Joint Object Search   CVPR 2017 Motivation: 传统的 bottom-up object region proposals 的方法,由于提取了较多的 proposal,导致后续计算必须依赖于抢的计算能力,如 GPU 等.那么,在计算机不足的情况下,则会导致应用范围受限.而 Active search method (就是 RL 的方法) 则提供了不错的方法,可以很大