Description: 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 Platform: |
Size: 141312 |
Author:秦元庆 |
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Description: 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. Platform: |
Size: 22528 |
Author:aa |
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Description: 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 Platform: |
Size: 990208 |
Author:陈金森 |
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Description: 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 Platform: |
Size: 1095680 |
Author:胡桃 |
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Description: 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 Platform: |
Size: 41228288 |
Author:赵寿玲 |
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Description: 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.
Platform: |
Size: 29696 |
Author:崔暁魏 |
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Description: 强化学习在认知无线电领域的应用，很有价值的英文参考资料。-Reinforcement learning in the field of cognitive radio applications, valuable reference materials in English. Platform: |
Size: 292864 |
Author:feixiang |
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Description: Reinforcement learning, a Q learning algorithm, implementation on a robot that tryies to solve randomly created maze and reach the goal. Note that you can run .m files both on Matlab and Octave. Platform: |
Size: 36864 |
Author:YH.HO |
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Description: 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.) Platform: |
Size: 8673280 |
Author:Gjessing |
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