Tamia  Walter

Tamia Walter

1602736380

How to Build a Pokémon Game and Learn Vyper in the Process

Last month, I started exploring DeFi.

There was a lot of fuss going around in my twitter feed, so I wanted to see what was going on under the hood.

While checking out different the DeFi projects I came across Vyper, a relatively new smart contract language. I had heard about it before but never used it.

I looked into it and was impressed by its security-first principals. I wanted to learn more, so I searched for articles, videos. But most of them were outdated.

There were no good resources available…except the documentation. But, to be honest, that’s the last place you would want to learn from.

I remember the time I started learning Solidity from CryptoZombies and and Ethernaut.

There was nothing like this for Vyper.

So we started building one.

Vyper.fun is a website where anyone can learn Vyper, even if it is their first language.

To make the learning experience interesting, you will build a Pokémon game on the blockchain, from scratch. A game in which Pokémon trainers battle with wild Pokémon to defeat and capture them.

In each chapter, you will learn a concept. You will use those concepts to build the game in the in-build code-editor.

You can write your code in 🛠 Your code tab and check the solution in the ✅ Solution tab. You can check the difference between 🛠 Your code and ✅ Solution in the 𝌡 Difference tab.

If you have any doubt or have any suggestions on how to improve the website, you can open the Gitter chat within the website…without having to leave the website 🤯

#vyper #smart-contracts #ethereum #vyper-language #blockchain #blochchain-games #blockchain-gaming #ethereum-blockchain-games

What is GEEK

Buddha Community

How to Build a Pokémon Game and Learn Vyper in the Process
Tamia  Walter

Tamia Walter

1602736380

How to Build a Pokémon Game and Learn Vyper in the Process

Last month, I started exploring DeFi.

There was a lot of fuss going around in my twitter feed, so I wanted to see what was going on under the hood.

While checking out different the DeFi projects I came across Vyper, a relatively new smart contract language. I had heard about it before but never used it.

I looked into it and was impressed by its security-first principals. I wanted to learn more, so I searched for articles, videos. But most of them were outdated.

There were no good resources available…except the documentation. But, to be honest, that’s the last place you would want to learn from.

I remember the time I started learning Solidity from CryptoZombies and and Ethernaut.

There was nothing like this for Vyper.

So we started building one.

Vyper.fun is a website where anyone can learn Vyper, even if it is their first language.

To make the learning experience interesting, you will build a Pokémon game on the blockchain, from scratch. A game in which Pokémon trainers battle with wild Pokémon to defeat and capture them.

In each chapter, you will learn a concept. You will use those concepts to build the game in the in-build code-editor.

You can write your code in 🛠 Your code tab and check the solution in the ✅ Solution tab. You can check the difference between 🛠 Your code and ✅ Solution in the 𝌡 Difference tab.

If you have any doubt or have any suggestions on how to improve the website, you can open the Gitter chat within the website…without having to leave the website 🤯

#vyper #smart-contracts #ethereum #vyper-language #blockchain #blochchain-games #blockchain-gaming #ethereum-blockchain-games

Sival Alethea

Sival Alethea

1624323600

Learn Python by Building Five Games - Full Course. DO NOT MISS!!!

Learn Python in this full tutorial course for beginners. This course takes a project-based approach. We have collected five great Python game tutorials together so you can learn Python while building five games. If you learn best by doing, this is the course for you.
⭐️ Course Contents ⭐️
⌨️ (0:01:18) Pong
⌨️ (0:45:36) Snake
⌨️ (1:34:57) Connect Four
⌨️ (2:42:36) Tetris
⌨️ (4:22:12) Online Multiplayer Game
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=XGf2GcyHPhc&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=4
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#python #games #learn python by building five game #project-based approach #game in python #learn python by building five games - full course

Tia  Gottlieb

Tia Gottlieb

1595573880

Deep Reinforcement Learning for Video Games Made Easy

In this post, we will investigate how easily we can train a Deep Q-Network (DQN) agent (Mnih et al., 2015) for Atari 2600 games using the Google reinforcement learning library Dopamine. While many RL libraries exist, this library is specifically designed with four essential features in mind:

  • Easy experimentation
  • Flexible development
  • Compact and reliable
  • Reproducible

_We believe these principles makes __Dopamine _one of the best RL learning environment available today. Additionally, we even got the library to work on Windows, which we think is quite a feat!

In my view, the visualization of any trained RL agent is an absolute must in reinforcement learning! Therefore, we will (of course) include this for our own trained agent at the very end!

We will go through all the pieces of code required (which is** minimal compared to other libraries**), but you can also find all scripts needed in the following Github repo.

1. Brief Introduction to Reinforcement Learning and Deep Q-Learning

The general premise of deep reinforcement learning is to

“derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations.”

  • Mnih et al. (2015)

As stated earlier, we will implement the DQN model by Deepmind, which only uses raw pixels and game score as input. The raw pixels are processed using convolutional neural networks similar to image classification. The primary difference lies in the objective function, which for the DQN agent is called the optimal action-value function

Image for post

where_ rₜ is the maximum sum of rewards at time t discounted by γ, obtained using a behavior policy π = P(a_∣_s)_ for each observation-action pair.

There are relatively many details to Deep Q-Learning, such as Experience Replay (Lin, 1993) and an _iterative update rule. _Thus, we refer the reader to the original paper for an excellent walk-through of the mathematical details.

One key benefit of DQN compared to previous approaches at the time (2015) was the ability to outperform existing methods for Atari 2600 games using the same set of hyperparameters and only pixel values and game score as input, clearly a tremendous achievement.

2. Installation

This post does not include instructions for installing Tensorflow, but we do want to stress that you can use both the CPU and GPU versions.

Nevertheless, assuming you are using Python 3.7.x, these are the libraries you need to install (which can all be installed via pip):

tensorflow-gpu=1.15   (or tensorflow==1.15  for CPU version)
cmake
dopamine-rl
atari-py
matplotlib
pygame
seaborn
pandas

#reinforcement-learning #q-learning #games #machine-learning #deep-learning #deep learning

Swati patel

1625050825

What is Game Based Learning (GBL) - Benefit & Example of Game Base Learning

“Game Based Learning is the future of EdTech and eLearning. Explore its many benefits and examples to unlock its true potential and transform your learning best experience.”

https://www.communicationcrafts.com/benefit-example-of-game-base-learning-gbl/?cc=com&?utm_source=morioh&utm_medium=SBM&utm_campaign=Game-Based-Learning:-The-Future-of-EdTech-in-eLearning

##gbl ##game based learning ##learning ##edtech ##edtech learning ##learning experience

Tamale  Moses

Tamale Moses

1618698960

Playground Games and Turn 10 Studios respectively improved time on Visual Studio 2019

The C++ team at Visual Studio has delivered substantial build and link time improvements throughout Visual Studio 2019. This blog is Part 2 of a series of blogs showcasing real-world results of our efforts. See how the Gears 5 team benefited from iteration build time improvements in Part 1.

#c++ #performance #build throughput #build time #compile time #game development #games #gaming #iteration time #linker #video games