Leveraging Machine Learning for game development

Game state representation

Over the years, online multiplayer games have exploded in popularity, captivating millions of players across the world. This popularity has also exponentially increased demands on game designers, as players expect games to be well-crafted and balanced — after all, it’s no fun to play a game where a single strategy beats all the rest.

Normally, identifying imbalances in a newly prototyped game can take months of playtesting. With this approach, we were able to not only discover potential imbalances but also introduce tweaks to mitigate them in a span of days. We found that a relatively simple neural network was sufficient to reach high level performance against humans and traditional game AI.

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Utilizing AI for the Protection of Service Members and Civilians through unmanned Self-Driving Software

Autonomous console

Shield AI is a California-based startup employing artificial intelligence to develop products that offer protection for service members and civilians. It is primarily a defense technology organization that uses self-driving software, enabling the unmanned systems to operate smoothly even without the availability of GPS or any other form of communication.

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A practical guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination.

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Texas A&M Reinforcement Learning Algorithm automates Oil and Gas reserve forecasting

Oil and Gas

Oil and gas extraction is a messy business, not least because much of the initial discovery process relies on educated guesswork that often proves fruitless. O&G companies are always searching for ways to reduce these false positives and thereby the costs of their discovery operations – and now, a team of researchers at Texas A&M University have produced a new algorithm that automates prediction of oil and gas reserves.

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Achieving super-human performance in QWOP using Reinforcement Learning and Imitation Learning

Human Performance

QWOP is a simple running game where the player controls a ragdoll’s lower body joints with 4 buttons. The game is surprisingly difficult and shows the complexity of human locomotion. Using machine learning techniques, I was able to train an AI bot to run like a human and achieve a finish time of 47 seconds, a new world record. This article walks through the general approach as well as the training process. Huge thanks to Kurodo (@cld_el), one of the world’s top speedrunners, for donating encoded game recordings to help train the agent.

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Guide to MBIRL – Model Based Inverse Reinforcement Learning

In this article, we are going to discuss one such algorithm-based Inverse Reinforcement Learning. The proposed MBIRL algorithm learns loss functions and rewards via gradient-based bi-level optimization.  This framework builds upon approaches from visual model-predictive control and IRL. This new MBIRL algorithm is a collaborative work of Neha Das (Facebook AI Research)*; Sarah Bechtle (Max Planck Institute for Intelligent Systems); Todor Davchev (University of Edinburgh); Dinesh Jayaraman (University of Pennsylvania); Akshara Rai (Facebook); Franziska Meier (Facebook AI Research) and was accepted at 4th Conference on Robot Learning (CoRL 2020), Cambridge MA, USA in a Conference Paper.

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How reinforcement learning chooses the ads you see

Every day, digital advertisement agencies serve billions of ads on news websites, search engines, social media networks, video streaming websites, and other platforms. And they all want to answer the same question: Which of the many ads they have in their catalog is more likely to appeal to a certain viewer? Fortunately (for the ad agencies, at least), reinforcement learning,…

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Mastering Atari with discrete World Models

Deep reinforcement learning (RL) enables artificial agents to improve their decisions over time. Traditional model-free approaches learn which of the actions are successful in different situations by interacting with the environment through a large amount of trial and error. In contrast, recent advances in deep RL have enabled model-based approaches…

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Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study…

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DeepMind & UCL’s Alchemy Is a ‘Best-of-Both-Worlds’ 3D Video Game for Meta-RL

Deepmind Alchemy

n recent years, reinforcement learning (RL) has garnered much attention in the field of machine learning. The approach does not require labelled data and has yielded remarkable successes on a wide variety of specific tasks. RL unfortunately continues to struggle with issues such as sample efficiency, generalization, and transfer learning. To address these drawbacks, researchers have been exploring meta-reinforcement learning (meta-RL), in which learning strategies can quickly adapt to novel tasks by using experience gained on a large set of tasks that have a shared structure.

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AI Agents Play “Hide the Toilet Plunger” to Learn Deep Concepts About Life

Most papers about artificial intelligence don’t cite Jean Piaget, the social scientist known for his groundbreaking studies of children’s cognitive development in the 1950s. But there he is, in a paper from the Allen Institute for AI (AI2). The researchers state that their AI agents learned the concept of object permanence—the understanding that an object hidden from view is still there—thus making those AI agents similar to a baby who just figured out the trick behind peekaboo.

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What is Artificial Intelligence? How does AI work, types and the future of it?

Head

The intelligence demonstrated by machines is known as Artificial Intelligence. Artificial Intelligence has grown to be very popular in today’s world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life.

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IBM’s AI learns to navigate around a virtual home using common sense

You know a shirt belongs in a wardrobe. I know a shirt belongs in a wardrobe. Does an AI know that? Typically, not. But it can learn by interacting with the world around it. We wanted to boost this technique, known as Reinforcement Learning, by injecting common sense into an AI model — and helping it to learn faster.

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Evaluating design trade-offs in Visual Model-Based Reinforcement Learning

Model-free reinforcement learning has been successfully demonstrated across a range of domains, including robotics, control, playing games and autonomous vehicles. These systems learn by simple trial and error and thus require a vast number of attempts at a given task before solving it. In contrast, model-based reinforcement learning (MBRL) learns a model of the environment (often referred to as a world model or a dynamics model) that enables the agent to predict the outcomes of potential actions, which reduces the amount of environment interaction needed to solve a task.

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7 challenges in Reinforcement Learning — and how researchers are responding

Right now, the personalized homepage for a popular e-commerce platform is recommending me a football pool toy alongside a pack of “self-sealing” water balloons. This despite the fact that I do not have kids, don’t own a pool and can see four inches of snow outside my window.
Most recommendation systems are very good at leveraging user history, but the less sophisticated ones aren’t as dynamic and responsive as they could be, especially when preferences change or new contexts emerge, like sudden snowstorms. Enter reinforcement learning.

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Reinforcement Learning — Teaching the Machine to Gamble — Q-learning

Reinforcement Learning is an area of ​​Artificial Intelligence and Machine Learning that involves simulating many scenarios in order to optimize the outcomes. One of the most used approaches in Reinforcement Learning is the Q-learning method. In Q-learning, a simulation environment is created and the algorithm involves a set of ‘S’ states for each simulating scenario, a set of ‘A’ actions, and an agent that takes these actions to permeate through the states.

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Reinforcement Learning — Teaching the Machine to gamble with Q-learning

Reinforcement Learning is an area of ​​Artificial Intelligence and Machine Learning that involves simulating many scenarios in order to optimize the outcomes. One of the most used approaches in Reinforcement Learning is the Q-learning method. In Q-learning, a simulation environment is created and the algorithm involves a set of ‘S’ states for each simulating scenario, a set of ‘A’ actions, and an agent that takes these actions to permeate through the states.

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And this, in a nutshell’s nutshell, is “Machine Learning”

Combining Part 1 and 2: Probably imagining a robot or terminator when asking about machine learning, in reality, machine learning is beyond and involved in almost all application you can imagine in our today world. Think of spam filter in your email, voice, face and fingerprint recognition on your phone, your Siri on iPhone, google assistance on android device etcetera have an iota of machine learning.

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