搜索资源列表
CS294
- 机器学习文档，CS294主要讲深度增强学习，课程实例很多，相信对学习有所帮助。(Machine Learning,Mainly speaking of deep reinforcement learning,,many examples of courses,I believe that learning helps.)
cartpole-dqn
- 利用deep q learning 的算法学习玩open ai gym里的cartpole游戏(Using deep Q learning algorithm to learn cartpole games in open AI gym)
ddpg
- 深度强化学习中DDPG算法的代码，用Python语言实现(The code of DDPG algorithm in deep reinforcement learning, implemented in Python language)
H-ELM
- 可用作数据分类和拟合，深度极限学习机拥有深度学习的优势和自身计算速度快的优势(It can be used to classify and fit data. The deep extrme learning machine has the advantages of depth learning and fast computing speed.)
pytorch-a2c-ppo-acktr-master
- 改代码为ACKTR代码，该算法比传统的TRPO和DQN在运行速度和计算量都有很大的提升(scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation)
V2V
- v2v信道建模，参数依据论文《Deep reinforcement learning based resource allocation in V2V communications》(V2V channel modelling, parameters are based on published paper Deep reinforcement learning based resource allocation in V2V communications)
DBN
- 深度信念网络，神经网络的一种。既可以用于非监督学习，类似于一个自编码机；也可以用于监督学习，作为分类器来使用。(Deep belief network, a kind of neural network. It can be used for unsupervised learning, similar to a self-coding machine, or supervised learning, as a classifier.)
Averaged-DQN_ Variance Reduction and Stabilization for Deep Reinforcement Learning
- Averaged-DQN论文，关于强化学习领域的文章。
tpprl
- 结合深度强化学习和时间点过程算法的实现，主要用python(In combination with deep reinforcement learning and implementation of time point process algorithm, Python is mainly used.)
wireshark网络分析就这么简单
- 《Wireshark网络分析就这么简单》采用诙谐风趣的手法，由浅入深地用Wireshark分析了常见的网络协议，读者在学习Wireshark的同时，也会在不知不觉中理解这些协议。作者还通过身边发生的一些真实案例，分享了Wireshark的实战技巧。 无论你是技术支持工程师、系统管理员、现场工程师、公司IT部门的老好人，还是高校网络相关专业的教师，无论你是CCNA、CCNP、CCIE，还是MCSE，《Wireshark网络分析就这么简单》都是迅速了解、掌握Wireshark技巧的绝佳读物。(
深度机器学习DBN
- 深度神经网络算法，可直接用用于模型训练，进行机器学习。算法可靠。(The deep neural network algorithm can be directly used for model training and machine learning. The algorithm is reliable.)
DGP-IRL-master
- We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.
FCMADDPG
- 基于深度强化学习的编队控制使用MADDPG算法(Formation control based on deep reinforcement learning)
9.14DQN-QL
- 深度强化学习一个简单的事例，用于深度强化学习而用(Deep reinforcement learning is a simple example for deep reinforcement learning)
ddpg
- 使用深度强化学习中的ddpg算法学习玩游戏，让智能体学习最优策略。(The ddpg algorithm in deep reinforcement learning is used to learn to play games, so that the agent can learn the optimal strategy.)