Build a Game AI - Machine Learning for Hackers #3

**Building an AI to Beat Atari Games: A Deep Dive into Reinforcement Learning**

Welcome to Sirajology! In this episode, we embark on an exciting journey to build an AI capable of mastering a variety of Atari games. This pursuit is significant because it challenges traditional AI methodologies and introduces innovative approaches that could pave the way for more versatile and powerful AI systems.

### Traditional Approaches: Limitations of Reductionist Models

Traditionally, game programmers have employed a reductionist approach to AI development. This method involves creating models based on specific simulated worlds, limiting the AI's adaptability to different environments. While this approach has been somewhat effective in confined contexts, it fails to generalize across diverse games and scenarios. For instance, an AI trained to excel at Pong might struggle when introduced to a completely different game like Space Invaders.

### DeepMind's Breakthrough: Introducing General AI

Enter DeepMind, a London-based startup that achieved a milestone in 2015 by developing an algorithm capable of mastering 49 different Atari games without any game-specific tuning. This breakthrough was made possible by the Deep Q Learner, an open-source algorithm available on GitHub. The algorithm's simplicity is striking: it requires only two inputs—raw pixels and scores—to maximize its objective, which is to achieve the highest score.

### How It Works: Reinforcement Learning in Action

The Deep Q Learner operates using reinforcement learning, a methodology inspired by how we train animals. Just as a dog learns through trial and error with rewards or punishments, this AI system adjusts its actions based on received rewards. Each time step involves executing an action based on observed game states, such as detecting enemies or scoring points.

At the core of this system is Q-Learning, which maps game states to optimal actions without prior knowledge of the environment. Experience replay further enhances learning by allowing the AI to draw from past experiences, much like how our hippocampus replays memories during sleep.

### Technical Components: Convolutional Neural Networks and Gym

The algorithm employs a deep convolutional neural network (CNN) to interpret game pixels. CNNs, inspired by the human visual cortex, efficiently process images by focusing on local regions rather than connecting every neuron. This reduces complexity and overfitting while building hierarchical feature representations, from edges to complex objects.

To implement this AI, we utilize TensorFlow for the neural network and Gym, OpenAI's library, for reinforcement learning setup. OpenAI, a nonprofit dedicated to creating general artificial intelligence (AGI), has contributed significantly to the field, with notable figures like Elon Musk supporting their mission.

### Building Your Game Bot: A Step-by-Step Guide

Let's get hands-on by building our game bot in just 10 lines of Python code. Begin by importing necessary libraries:

```python

import gym

from deep_q_network import DeepQNetwork

from deep_q_replay_buffer import ReplayBuffer

```

Initialize the environment with a chosen game, such as Space Invaders:

```python

env = gym.make("SpaceInvaders-v0")

agent = DeepQNetwork(env, env_type=" Atari")

trainer = Trainer(agent)

```

Start training by populating the replay memory and initializing the CNN:

```python

trainer.train()

```

This setup allows our AI to learn and adapt across various games, demonstrating the potential for creating versatile AI systems.

### Conclusion: The Future of AGI

DeepMind's achievement marks a crucial step toward developing artificial general intelligence (AGI). By focusing on learning mechanisms rather than game-specific models, we unlock the possibility of AI that can generalize across diverse tasks. This approach not only enhances gaming AI but also opens doors for applications in robotics, healthcare, and more, where adaptability is key.

Join us in exploring this frontier as we continue to innovate and push the boundaries of what AI can achieve. Stay tuned for more episodes where we delve deeper into these technologies and their implications for our future.

"WEBVTTKind: captionsLanguage: enYes I beat it, did that impress you? If i built an ai to beat this for me would that impress youHello World, welcome to Sirajology! In thisepisode we're going to build an AI to beata bunch of Atari games. Games have had a longhistory of being a testbed for AI ever sincethe days of Pong. Traditionally, game programmershave taken a reductionist approach to buildingAI. They've reduced the simulated world toa model and had the AI act on prior knowledgeof that model. And it worked out for the mostpart. I guess. Not really. But what if wewant to build an AI that can be used in severaldifferent types of game worlds? All the worldmodels are different so we couldn't feed itjust one world model. Instead of modelingthe world, we need to model the mind.We want to create an AI that can become apro at any game we throw at it. So in thinkingabout this problem, we have to ask ourselves-- what is the dopest way to do this? Well,the London-based startup DeepMind alreadydid this in 2015. DeepMind's goal is to createartificial general intelligence, thats onealgorithm that can solve any problem withhuman level thinking or greater. They reachedan important milestone by creating an algorithmthat was able to master 49 different Atarigames with no game-specific hyperparametertuning whatsoever. Google snapped them uplike yooooooooo. The algorithm is called theDeep Q Learner and it was recently made opensource on GitHub. It only takes two inputs-- the raw pixels of the game and the gamescore. That's it. Based on just that it hasto complete its objective; maximize the score.Let's dive into how this works, since we'llwant to recreate their results.First it uses a deep convolutional neuralnetwork to interpret the pixels. This is atype of neural network inspired by how ourvisual cortex operates, and expects imagesas inputs. Images are high dimensional dataso we need to reduce the number of connectionseach neuron has to avoid overfitting. Overfittingby the way is when your model is too complex,there too many parameters and so its overlytuned to the data you've given it and won'tgeneralize well for any new dataset. So unlikea regular neural network, a convolutionalnetwork's layers are stacked in 3 dimensionsand this makes it easy to connect each neuronONLY to neurons in its local region insteadof every single other neuron. Each layer actsas a detection filter for the presence ofspecific features in an image and the layersget increasingly abstract with feature representation.So the first layer could be a simple featurelike edges, then the next layer would usethose edges to detect simple shapes, and thenext one would use those shapes to detectsomething even more complex like Kanye. Thesehierarchical layers of abstraction are whatneural nets do really well.So once it's interpreted the pixels, it needsto act on that knowledge in some way. In aprevious episode we talked about supervisedand unsupervised learning. But wait (thereis another and his name is john cena) itscalled Reinforcement Learning. Reinforcementlearning is all about trial and error. Itsabout teaching an AI to select actions tomaximize future rewards. Its similar to howyou would train a dog. If the dog fetchesthe ball you give it a treat, if it doesn'tthen you withhold the treat. So while thegame is running, at each time step, the AIexecutes an action based on what it observesand may or may not receive a reward. If itdoes receive a reward, we'll adjust our weightsso that the AI will be likely to do a similaraction in the future. Q Learning is the typeof reinforcement learning that learns theoptimal action-selection behavior or policyfor the AI without having a prior model ofthe environment. So based on the current gamestate, like an enemy spaceship being in shootingdistance, the AI will eventually know to takethe action of shooting it. This mapping ofstate to action is its policy and it getsbetter and better with training. Deep Q alsouses something called experience replay, whichmeans the AI learns from the dataset of itspast policies as well. This is inspired byhow our hippocampus works, it replays pastexperiences during rest periods, like whenwe sleep.So we're going to build our game bot in just10 lines of Python using a combination ofTensorflow and Gym. Tensorflow is google'sML library which we'll use to create the convolutionalneural net, and Gym is OpenAI's ML librarywhich we'll use to create our reinforcementlearning algorithm and setup our environment.Oh, If you haven't heard, OpenAI is a non-profitAI research lab focused on creating AGI inan open source way. They've got a billionbucks pledged from people like Elon Musk soyeah. Elon Musk.Let's start off by importing our dependencies.Environment is our helper class that willhelp initialize our game environment. In ourcase, this will be space invaders, but wecan easily switch that out to a whole hostof different environments. Gym is very modular,OpenAI wants it to be a gym for AI agentsto train in and get better. You can submityour algorithm to their site for an evaluationand they'll 'score' it against a set of metricsserver-side. The more generalized the algorithm,the better -- and everybody's attempts canbe viewed online so it makes sharing and collaboratinga whole lot easier. I approve. We'll alsowant to import our deep q network helper classto help observe the game and our trainingclass to initialize the reinforcement learning.Once we've imported our dependencies, we cango ahead and initialize our environment. We'llset the parameter to space invaders. and theninitialize our agent using our DQN helperclass with the environment and environmenttype as the parameters. Once we have thatwe can start training by running the trainerclass with the agent as the parameter. First,this will populate our initial replay memorywith 50,000 plays so we have a little experienceto train with. Then it will initialize ourconvolutional neural network to start readingin pixels and our Q learning algorithm tostart updating our agent's decisions basedon the pixels it receives. This is an implementationof the classic \"agent-environment loop\". Eachtimestep, the agent chooses an action, andthe environment returns an observation anda reward. The observation is raw pixel datawhich we can feed into our convolutional network,and the reward is a number we can use to helpimprove our next actions. Gym neatly returnsthese parameters to use via the step functionwhich we've wrapped in the environment helperclass. During training, our algorithm willperiodically save the 'weights' to a filein the models directory so we'll always havea partially trained model at least.Expect it to take a few days to fully trainthis to human level. Once we've started training,we can start the game with the play functionof our agent object. We can go ahead and runthis in terminal and the space invaders windowshould pop up and we'll start seeing the AIstart attempting to play the game. It'll behilariously bad at first but will slowly getbetter with time. (terminator) We can seein terminal a set of metrics periodicallyprinted out so we can see how the agent isdoing as time progresses. The AI will getmore difficult to defeat the longer you trainit and ideally you can apply it to any gameyou create. Video games and other simulatedenvironments are the perfect testing groundsfor building AI since you can easily observeits behavior visually. For more info, checkout the links down below and please subscribefor more machine learning videos. For nowi've gotta go fix a runtime error so thanksfor watching\n"