reinforcement-learning

Inverse Reward Design: 强化学习中的Reward设计

在强化学习的MDP模型中,对于Reward函数的设计一直是智能体能否完成目标任务的核心关键,同时也是一个非常复杂的课题。目前非常多的强化学习算法都应用在规则明确的游戏领域,正是因为在这些场景下Reward非常好计算,比如围棋的输赢就是非常明确的。

read original article at https://medium.com/shidanqing/inverse-reward-design-%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0%E4%B8%AD%E7%9A%84reward%E8%AE%BE%E8%AE%A1-a6783cbdb596?source=rss——artificial_intelligence-5

Reinforcement Learning vs. Differentiable Programming

We’ve discussed the idea of differentiable programming, where we incorporate existing programs into deep learning models. But if you’re a…

read original article at https://medium.com/@ODSC/reinforcement-learning-vs-differentiable-programming-48528f464795?source=rss——artificial_intelligence-5

Summary on Dynamic Programming, Chapter 4 from Intro. to Reinforcement Learning — Sutton and Barto

The author introduces Dynamic Programming [1] a collection of algorithm which can be applicable to solve deterministic (finite) Markov…

read original article at https://medium.com/@ashutoshkakadiya/summary-on-dynamic-programming-chapter-4-from-intro-to-reinforcement-learning-sutton-and-barto-2d359e6ab529?source=rss——artificial_intelligence-5

Reinforcement Learning for Combinatorial Optimization

Learning strategies to tackle difficult optimization problems using Deep Reinforcement Learning and Graph Neural Networks.

read original article at https://towardsdatascience.com/reinforcement-learning-for-combinatorial-optimization-d1402e396e91?source=rss——artificial_intelligence-5

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