The in-context learning (ICL) capability of pre-trained models based on the transformer architecture has received growing interest in recent years. While theoretical understanding has been obtained for ICL in reinforcement learning (RL), the previous results are largely confined to the single-agent setting. This work proposes to further explore the in-context learning capabilities of pre-trained transformer models in competitive multi-agent games, i.e., in-context game-playing (ICGP). Focusing on the classical two-player zero-sum games, theoretical guarantees are provided to demonstrate that pre-trained transformers can provably learn to approximate Nash equilibrium in an in-context manner for both decentralized and centralized learning settings. As a key part of the proof, constructional results are established to demonstrate that the transformer architecture is sufficiently rich to realize celebrated multi-agent game-playing algorithms, in particular, decentralized V-learning and centralized VI-ULCB.

-- We propose an opinion-driven navigation framework for multi-robot traversal through a narrow corridor . Our approach leverages a multi-agent decision-making model known as the Nonlinear Opinion Dynamics (NOD) to address the narrow corridor passage problem, formulated as a multi-robot navigation game. By integrating the NOD model with a multi-robot path planning algorithm, we demonstrate that the framework effectively reduces the likelihood of deadlocks during corridor traversal. T o ensure scalability with an increasing number of robots, we introduce a game reduction technique that enables efficient coordination in larger groups. Extensive simulation studies are conducted to validate the effectiveness of the proposed approach. In recent years, robots have become increasingly integrated into human environments, including residential areas, healthcare facilities, and public spaces. As robots interact frequently with both humans and other robots, the importance of social navigation has grown significantly. Social navigation focuses on optimizing a robot's behavior to enhance human comfort and improve the acceptability of robots in shared spaces. For instance, when multiple robots navigate through a narrow corridor, as depicted in Figure 1, they must dynamically adapt their movements in response to the actions of others, ensuring smooth and cooperative interactions within such constrained environments.

We present Lode Encoder, a gamified mixed-initiative level creation system for the classic platform-puzzle game Lode Runner. The system is built around several autoencoders which are trained on sets of Lode Runner levels. When fed with the user's design, each autoencoder produces a version of that design which is closer in style to the levels that it was trained on. The Lode Encoder interface allows the user to build and edit levels through 'painting' from the suggestions provided by the autoencoders. Crucially, in order to encourage designers to explore new possibilities, the system does not include more traditional editing tools. We report on the system design and training procedure, as well as on the evolution of the system itself and user tests.

Not content with dominating our living rooms, kitchens and bedrooms, Ikea is coming for our video game dens. The flatpack furniture giant has announced a new collection aimed specifically at game players. Set for release in UK stores on 1 October, the range will feature more than 30 products including gaming desks, chairs and accessories. Ikea says it has collaborated closely with Republic of Gamers (ROG), a gaming sub-brand of tech company Asus, to ensure specific comfort features for game players. The emphasis on comfort is perhaps most explicitly realised in the Lånespelare neck pillow and multi-functional cushion/blanket – a sort of giant padded sofa anorak, which does little to combat the cliche of lazy slovenly gamers, but will be amazing for weekend-long Fortnite sessions.

The Electronic Entertainment Expo, better known as E3, finished its last day of presentations yesterday. For the first time in its 26 year history, E3 was an all-virtual event due to the COVID-19 pandemic, but that didn't stop the major game companies from delivering some (mostly) electrifying news. Tiny Tina's Wonderlands was one of the games players caught a first glimse of at this year's E3. Tiny Tina's Wonderlands was one of the games players caught a first glimse of at this year's E3. Starting with the Summer Games Fest's Kickoff Live event, audiences caught the first glimpse of developer Gearbox Software's Tiny Tina's Wonderlands, a genial sounding game that appears to anything but; the trailer opens with a dreadlocked warrior blasting a machine gun at a dragon shooting electricity.

Counterfactual regret minimization (CFR) is a popular method to deal with decision-making problems of two-player zero-sum games with imperfect information. Unlike existing studies that mostly explore for solving larger scale problems or accelerating solution efficiency, we propose a framework, RLCFR, which aims at improving the generalization ability of the CFR method. In the RLCFR, the game strategy is solved by the CFR in a reinforcement learning framework. And the dynamic procedure of iterative interactive strategy updating is modeled as a Markov decision process (MDP). Our method, RLCFR, then learns a policy to select the appropriate way of regret updating in the process of iteration. In addition, a stepwise reward function is formulated to learn the action policy, which is proportional to how well the iteration strategy is at each step. Extensive experimental results on various games have shown that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.

In this article, to answer these questions, we go through the Monte Carlo Tree Search fundamentals. Since in the next articles, we will implement this algorithm on "HEX" board game, I try to explain the concepts through examples in this board game environment. If you're more interested in the code, find it in this link. There is also a more optimized version which is applicable on linux due to utilizing cython and you can find it in here. Monte Carlo method was coined by Stanislaw Ulam for the first time after applying statistical approach "The Monte Carlo method".

Simulating a human gamer has enabled Candy Crush developer King to speed up its release cycles. The evolution of DeepMind's AlphaGo deep learning algorithm was the inspiration behind mobile games developer King's work to build a simulation of a games player using Google's TensorFlow. AlphaGo beat Go world champion Lee Sedol in 2016. To simulate the ancient game of Go, AlphaGo needed to play the game over and over again, applying a technique called a Monte Carlo search, which uses a deep neural network to "learn" what is the best play move to make. At the time, artificial intelligence (AI) researcher Demis Hassabis, co-founder of DeepMinds, which Google acquired in 2014, described how open source libraries for numerical computation using data flow graphs, such as TensorFlow, allow researchers to efficiently deploy the computation needed for deep learning algorithms across multiple CPUs or GPUs. According to GitHub's Octoverse 2018 report, TensorFlow was by far the most popular open source project in 2018.

Every year, many of my clients ask me about the key technology trends that I believe will define the coming year. Here are my predictions of the tech trends that have the potential to make or break careers and businesses in 2019. While some of these may seem obvious – no one will be surprised to hear that artificial intelligence (AI) and machine learning are likely to remain hot topics – the disruptive nature of tech means it's likely we will get a few surprises. The year 2018 will be remembered as the year that artificial intelligence (AI) hit the mainstream, but in 2019 and beyond it's going to be absolutely everywhere. This is because hardware and software developers have passed the trial period – experimenting to see where AI fits, and where it can deliver the biggest improvements in customer experience and productivity improvements.

Trump's election ushered in a political winter doomed to last at least four years, assuming he escapes impeachment. Since then, creatives in virtually every industry have responded by turning Trump's inflammatory soundbites into kindling for the artistic fire. TV shows such as Netflix's Dear White People and The Handmaid's Tale have played on the anxieties induced by the barely veiled misogyny and racism in his rhetoric. In cinema we see films such as BlacKkKlansman, Battle of the Sexes and The Post capturing the tension of the era with prescience, given their long production cycles. Resistance politics has also erupted off the screen in the #MeToo movement.