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[AI-NN-PR] muti-agents-toolbox DL : 1: 多智能体工具包，可直接用来进行多智能体强化学习算法设计与仿真-Multi-agent toolkit, can be directly used for multi-agent reinforcement learning algorithm design and simulation
Update : 2025-02-17 Size : 138kb Publisher : 秦元庆
[source in ebook] RL_sim DL : 0: Reinforcement Learning
Update : 2025-02-17 Size : 1.62mb Publisher : liuzhigang
[Other Riddle games] kevin DL : 0: 基于强化学习的robocode代码，实现了一个通过RL进行动作选择的坦克，导入robocode的相关文件夹下即可执行。 -Based on Reinforcement Learning robocode code, implements a RL to move through the choice of tanks, robocode into the relevant folder can be executed.
Update : 2025-02-17 Size : 22kb Publisher : aa
[AI-NN-PR] RL_Toolbox_Windows_Source_20b DL : 1: 强化学习算法（R-Learning）难得的珍贵资料，在国外找的。分享给大家。-Reinforcement learning algorithm (R-Learning) rare precious materials, looking abroad. Share with everyone.
Update : 2025-02-17 Size : 11.83mb Publisher : michile
[Algorithm] reinforcement DL : 1: 贝叶斯学习与强化学习结合技术的研详细介绍了强化学习的算法和理论-Bayesian learning and reinforcement learning combined with the technical details of the study of reinforcement learning algorithms and theory
Update : 2025-02-17 Size : 967kb Publisher : 陈金森
[Other] reinforcement_learning_tutoial DL : 1: reinforcement learning tutoial book!
Update : 2025-02-17 Size : 184kb Publisher : Ali
[Documents] RL2 DL : 0: reinforcement learning
Update : 2025-02-17 Size : 210kb Publisher : hamid
[Algorithm] 2007-03-23_approxrl DL : 0: approximate reinforcement learning
Update : 2025-02-17 Size : 63kb Publisher : controlman22
[AI-NN-PR] Torch3win DL : 0: the example and toolbox for machine learning（under windows platform）including all the widely used method such as monte carlo method,reinforcement learning method and so on
Update : 2025-02-17 Size : 1.04mb Publisher : 胡桃
[AI-NN-PR] ReinforcementLearning DL : 0: 这是机器学习里面的强化学习的重要资料，主要介绍了强化学习的基本理论及实验和分析-This is inside the machine learning reinforcement learning important information, which mainly describes the basic theory of reinforcement learning and experimental and analytical
Update : 2025-02-17 Size : 39.32mb Publisher : 赵寿玲
[Other] main DL : 2: 在各种非线性系统中，倒立摆是一典型例子，用强化学习实现控制倒立摆-on-line learning control by association and reinforcement
Update : 2025-02-17 Size : 3kb Publisher : xueguoying
[Windows Develop] Buterfly DL : 0: Artificial intelligence with Reinforcement learning.
Update : 2025-02-17 Size : 182kb Publisher : khoarus
[Books] Reinforcmentlearning DL : 0: Reinforcement learning a detail understanding
Update : 2025-02-17 Size : 156kb Publisher : waqar-ul-haq
[Windows Develop] SuttonBArtoReinforcementlearning DL : 0: sutton and barto books on reinforcement learning
Update : 2025-02-17 Size : 1.82mb Publisher : waqar-ul-haq
[AI-NN-PR] Q_learning DL : 0: 强化学习是人工智能中策略学习的一种，基于预期最大利益原则。和博弈论有密切的关系，也是多主体系统学习的常用方法。-Reinforcement learning is a kind of artificial intelligence in the strategic study, based on the principle of best interests is expected. And game theory are closely related, but also multi-agent systems common method of learning.
Update : 2025-02-17 Size : 29kb Publisher : 崔暁魏
[Mathimatics-Numerical algorithms] RL DL : 0: 采用vs2008 c#编写 Q-Learning强化学习的代码实现状态空间5维，动作空间5维-Using vs2008 c# Q-Learning reinforcement learning the code to achieve the state-space five-dimensional, five-dimensional action space
Update : 2025-02-17 Size : 51kb Publisher : 王大山
[AI-NN-PR] TicTacToe DL : 0: 将强化学习算法应用于Tic-tac-toe游戏-The reinforcement learning algorithm is applied to Tic-tac-toe game
Update : 2025-02-17 Size : 73kb Publisher : 荆玲
[matlab] Q-learning-Reinforcement-learning DL : 6: 强化学习matlab源代码很少见的源代码，详细介绍Q学习的编程过程。-Reinforcement learning matlab source code，rare source code, detailed Q learning programming.
Update : 2025-02-17 Size : 1kb Publisher : jzhao
[Other] Reinforcement-Learning DL : 0: Reinforcement Learning
Update : 2025-02-17 Size : 3.59mb Publisher : Mehdi
[Documents] Reinforcement Learning：An Introduction DL : 0: 在这本书中，我们探索了一种从交互中学习的计算方法。我们不直接对人或动物如何学习进行理论分析，而是探索理想化的学习情境，评估各种学习方法的效率。也就是说，我们采用人工智能研究人员或工程师的角度。我们探索去设计在这些方面上格外有效率的机器，他能够解决科学或经济学领域的问题。通过数据分析和计算实验来评估这些设计。我们将这种方法称为强化学习，更侧重于目标导向的交互学习，而不是其他方法。(In this book, we explored a computational method of learning from interaction. We do not directly analyze how people or animals learn, but explore idealized learning situations and evaluate the efficiency of various learning methods. That is to say, we adopt the angle of artificial intelligence researchers or engineers. We are exploring ways to design machines that are exceptionally efficient in these areas and that can solve problems in science or economics. These designs are evaluated through data analysis and computational experiments. We call this method reinforcement learning, which focuses more on goal-oriented interactive learning than other methods.)
Update : 2025-02-17 Size : 8.27mb Publisher : Gjessing

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