Reinforcement Learning Algorithms - Explore key reinforcement learning algorithms in machine learning, including Q-learning, Deep Q-Networks, and Policy Gradients. Understand their applications and methodologies.
这是我的Github仓库：https://github.com/XinJingHao/Deep-Reinforcement-Learning-Algorithms-with-Pytorch...
Policies and Learning Algorithms Introduction to reinforcement learning algorithms and neural network policies. 14:50Video length is 14:50 The Walking Robot Problem Application of reinforcement learning for robotics, and specifically for bipedal robot walking. ...
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List of Computer Science courses with video lectures. computer-sciencesecuritymachine-learningbioinformaticsweb-developmentreinforcement-learningcomputer-visiondeep-learningalgorithmsroboticscomputational-biologydatabasesembedded-systemssystemscomputational-physicsquantum-computingcomputer-architecturedatabase-systems ...
模仿学习。reward function在强化学习里面非常非常重要,是对行为的抽象精简的描述,因此IRL (Inverse Reinforcement Learning)可能是一种很高效的模仿学习范式。 III) 一些强化学习相关名词的定义: (包括:MDP,policy,value function,q-function,optimal value function, optimal q-function,Bellman equations, Bellman Optimal...
Fundamental algorithms such as sorting or hashing are used trillions of times on any given day1. As demand for computation grows, it has become critical for these algorithms to be as performant as possible. Whereas remarkable progress has been achieved i
A collection of Meta-Reinforcement Learning algorithms in PyTorch snailmeta-reinforcement-learningrl2mgrl UpdatedJul 16, 2024 Python WangJingyao07/Meta-Learning-Papers-with-Code Star34 🎉🎨 This repository contains a reading list of papers with code on **Meta-Learning** and ***Meta-Reinforcement...
Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts wi
You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning. Enroll in course ...