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Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps.
deepmind 在2013年的Playing Atari with Deep Reinforcement Learning提出的DQN算是DRL的一个重要起点了,也是理解DRL不可错过的经典模型了。网络结构设计方面,DQN之前有些网络是左图的方式,输入为S,A,输出Q值;DQN采用的右图的结构,即输入S,输出是离线的各个动作上的Q值。之所以这样,左图方案相对右图最大的缺点是对于...
文章要点:这篇文章想说之前那些衡量RL算法的指标(rawreward, avgreward,maximum rawreward等等)不好,只看得出来一个得分,反映不出来RL在训练过程中的问题。然后作者自己设计了几个指标来检测RL在训练中可能出现的问题(detect anomalies during the training process automatically)。
GitHub - songrotek/rllab: rllab is a framework for developing and evaluating reinforcement learning algorithms. GitHub - songrotek/DRL-FlappyBird: Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using Tensorflow) ...
Deep Reinforcement Learning Algorithms Here you can find several projects dedicated to the Deep Reinforcement Learning methods. The projects are deployed in the matrix form: [env x model], where env is the environment to be solved, and model is the model/algorithm which solves this environment. ...
PyTorch implementations of deep reinforcement learning algorithms and environments - p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
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...
同步并行学习 (Synchronized parellel learning): 通过使用并行工作器 (Parallel worker) 来获得批量数据,每一个模拟器 (Simulator) 执行相同的 Policy,然后更新价值函数并同步更新策略 异步并行学习 (Asynchronous parallel actor-critic): 由于避免了同步操作,各个线程以其自己的速度运行,更新是拉取最新的参数进行更新;...
强化学习(Reinforcement Learning,RL)简单来说是对Agents(智能体)的研究,研究它们如何在环境中试错然后进行学习——我们认为奖励或惩罚Agent的行为可以让它在未来继续或停止这些行为。一些典型的RL应用比如AlphaGo,以及OpenAI做的Dota2 AI,都非常有名,RL目前在这些策略性游戏上取得了非常不错的成果。 2.1 核心概念和术语...