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1. A series of articles about getting started with DQN:DQN from getting started to giving up2. Introductory Paper2.1 Playing Atariwith a deep reinforcement learning DeepMind published in Nips 2013, the first time in this paper Reinforcement learning this name, and proposed DQN (deep q-network) algorithm, realized from pure image input completely through learning to play Atari game results 2.2 Human-level Control through deep Reinforcement learningThe

algorithm, there are many difficulties, such as computational power, such as conceptual understanding. For example, learn some joint probabilities. I think we are still floating on the surface of the optimization problem of training super-large-scale neural networks. Then the enhancement study, very useful, needs to be improved. You can see the recent work of DeepMind company, they use neural network to automate the 80 's Atari game, very interesting

" (Donkey Kong) I am playing. Cpu FC uses MOS 6502 (clocked at 1.79MHz) as its CPU. 6502 is a 8-bit microprocessor that was born in 1975 (40 years ago). At that time the chip was very popular, not only for FC, but also widely used in Atari 2600 800, Apple I II, Commodore 64, VIC-20, BBC micro and other machines. In fact, until today the 6502 revision (65C02) is still in production. 6502 registers are relatively small, only registers A, X, and Y, and

similar to the AXP Platform mentioned above. The possible difference lies in the endian, because the reason for the difference is big endian, while the AXP and ix86 are little endian. 　　 　　 Linux Ethernet card on other hardware Other hardware platforms can run Linux, such as Atari/Amiga (m68k ). Just like a Linux instance, it is best to visit the homepage of each platform supported by Linux to see which hardware is currently supported. (Welcome to pr

Article title: LinuxKernel2.6.25.9. Linux is a technology channel of the IT lab in China. Includes basic categories such as desktop applications, Linux system management, kernel research, embedded systems, and open source. Linux Kernel is the core component of Linux system, supporting Intel, Alpha, PPC, iSCSI, IA-64, arm, MIPS, Amiga, Atari and IBM s/390, etc, it also supports 32-bit large file systems. on the Intel platform, the maximum physical mem

Article title: LinuxKernel2.6.25.7. Linux is a technology channel of the IT lab in China. Includes basic categories such as desktop applications, Linux system management, kernel research, embedded systems, and open source. Linux Kernel is the core component of Linux system, supporting Intel, Alpha, PPC, iSCSI, IA-64, arm, MIPS, Amiga, Atari and IBM s/390, etc, it also supports 32-bit large file systems. On the Intel platform, the maximum physical mem

macro; Rapidcomm sound fileREG: Registry FileRep:visual dBASE Report FileRes:microsoft Visual C + + resource filesRm:realaudio Video filesRmf:rich map Format (3D game Editor uses it to save diagrams)Rom: cassette-based home game emulator file (full copy of ROM from Atari 2600, ColecoVision, Sega, Nintendo, etc. cassette tapes, which cannot be modified between two emulators)RXX: RAR archive Files on multiple volumes (a number between xx=1~99)SSAV: Gam

lossless. Lzo supports repeated compression and in-situ decompression. Lzo is a block compression algorithm-compressing and compressing data into blocks. The size of the Block Used for compression and decompression must be the same. Lzo compresses data blocks into a sequence of matched data (slide dictionary) and non-matched text. Lzo provides special processing for long matching data and long non-matching text sequences, which can achieve good results for highly redundant data, in additi

Dueling Network architectures for deep reinforcement learningICML Best PaperGoogle DeepMind Abstract: This article is one of ICML 2016 's best papers and is also from Google DeepMind.In recent years, on the reinforcement learning on the deep representation have achieved great success. However, many of these applications take advantage of traditional network architectures, such as neural networks, LSTMS, or auto-encoders. In this paper, a new network structure is proposed to deal wi

model of prior knowledge, at least according to the prior knowledge we can determine how much input can lead to how much output. For example play Atari this game, if the input only half of the screen, then we know that no matter how good the algorithm, can not be trained. Because the input is limited, and even humans cannot do it. But at the same time, humans do not need to know exactly what the exact model should be, and humans can deduce the result

Kernel release: LinuxKernel2.6.25.7 -- Linux general technology-Linux programming and kernel information. For details, refer to the following section. Linux Kernel is the core component of Linux system, supporting Intel, Alpha, PPC, iSCSI, IA-64, ARM, MIPS, Amiga, Atari and IBMs/390, etc, it also supports 32-bit large file systems. on the Intel Platform, the maximum physical memory is 64 GB. enhanced support for IDE and SCSI hardware systems and enhan

Linux Kernel updates are getting faster and faster. Due to the popularity of Linux, everyone is paying attention to it and there are more and more security risks. Supports Intel, Alpha, PPC, iSCSI, IA-64, ARM, MIPS, Amiga, Atari, IBM s/390, and 32-bit large file systems. On the Intel Platform, the maximum physical memory support is 64 GB. Enhanced support for IDE and SCSI hardware systems and enhanced support for USB devices and 3D accelerator cards

exceed the limit of the FAT table, the Mformat will automatically enlarge the number of magnetic regions. -S -m software magnetic area size. This number is the size of the magnetic field in which the system returns. is usually the same as the actual size. -A If this parameter is added, the Mformat will produce a set of Atari system serial number to the soft disk. -X formats the floppy disk into XDF format. You must first use the xdfcopy i

anyone because HTML5 not perfect not to develop HTML5 game, if recall, people first use Atari and Commodore 64 when the game, how many restrictions, in fact, in the development of the process to come up with a variety of ideas. We can do a lot of things with HTML5 now, and I think the only thing now is for developers to focus on it and not worry about its limitations. What causes developers to be less interested in HTML5 development I think there a

deepmind about the DQN mixed story http://tech.ifeng.com/a/20171010/44710270_0.shtmlTake a picture, everybody, look.The horizontal axis is the number of training times, the longitudinal axes are more than the human level percentage, 100% is equal to the average human playing game level, in the 57 Atari games average performance.So much to talk about today, and then I will continue to add a summary of the algorithm, thank you for reading!Gans: Generat

the algorithm to AI clearance in a variety of small games in the Atari game console.The core of its basic algorithm is the CNN we introduced before and the q-learning of enhanced learning, the basic flow of game intelligent customs clearance, such as:Use CNN to identify the state of the game's total Mario and use the enhanced learning algorithm to make the action selection, then calculate the reward function based on the new return state and the hist

, you need to go back to the 70 's. Interestingly, the prototype design with the rapid development of computer technology, but also the high density of the iterative upgrade, and then I will list the development of the information age of the big events, they are also a major impact on the prototype design milestones. 1970--Waterfall model becomes the mainstream in software development field The importance of 1975--information architecture was recognized and began to develop 1980--as a result

take on a task. We introduce feudal Networks (funs): A novel architecture for hierarchical reinforcement. Our approach are inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy B Y decoupling End-to-end learning across multiple levels allowing it to utilise different of. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and enacted by the