opponent


AI Technology in the Gaming Industry - Armchair Arcade

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AI technology has been making strides at a rapid pace with every passing day. AI isn't merely an idea anymore, some fanciful, futuristic thing that will always be out of reach, it is all around us, we use it every day. AI is used in the financial sector, cyber security, medicine, e-commerce, manufacturing, and of course, gaming. It is able to make medicine, protect your money, drive your car, or allows your fridge to tell you you're low on milk. Currently, AI is being heavily used in the gaming, medicine, and security fields.


AI Is Now the Undisputed Champion of Computer Chess

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It was a war of titans you likely never heard about. One year ago, two of the world's strongest and most radically different chess engines fought a pitched, 100-game battle to decide the future of computer chess. On one side was Stockfish 8. This world-champion program approaches chess like dynamite handles a boulder--with sheer force, churning through 60 million potential moves per second. Of these millions of moves, Stockfish picks what it sees as the very best one--with "best" defined by a complex, hand-tuned algorithm co-designed by computer scientists and chess grandmasters.


Online Reinforcement Learning in Stochastic Games

Neural Information Processing Systems

We study online reinforcement learning in average-reward stochastic games (SGs). An SG models a two-player zero-sum game in a Markov environment, where state transitions and one-step payoffs are determined simultaneously by a learner and an adversary. We propose the \textsc{UCSG} algorithm that achieves a sublinear regret compared to the game value when competing with an arbitrary opponent. This result improves previous ones under the same setting. The regret bound has a dependency on the \textit{diameter}, which is an intrinsic value related to the mixing property of SGs.


Machine Learning with Starbursts

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Years ago in college, I took a course on the Philosophy of A.I. and Machine Learning. This course had me thinking about computers in ways I'd never thought of them before--not as abstract, magical black boxes, but as mechanical devices using simple rules. Understanding how these simple rules lead to complex processes is the key to understanding machine learning. In this article I want to demonstrate the basics of Machine Learning -- one of the more advanced and cutting edge areas of Computer Science. And as in the previous articles of this series, we're not going to need any computers.


No Human Being Can Beat Google s AlphaGo, and It's a Good Thing

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South Korean Go master Lee Se-Dol recently announced his retirement from professional Go competition. He felt that no matter how hard he tries, he will never beat AI Go players like AlphaGo. It is a rather sad decision and development of his historical defeat in competition with Google DeepMind's AlphaGo. It gives the whole thing a more dramatic tone than it should be. However, the defeat of human Go players to AI is neither the end of the world for the Go game nor for the human players.


Explained: The Artificial Intelligence Race is an Arms Race

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Most chess computers play a purely mathematical strategy in a game yet to be solved. They are raw calculators and look like it too. AlphaZero, at least in style, appears to play every bit like a human. It makes long-term positional plays as if it can visualize the board; spectacular piece sacrifices that no computer could ever possibly pull off, and exploitative exchanges that would make a computer, if it were able, cringe with complexity. In short, AlphaZero is a genuine intelligence.


Variational Autoencoders for Opponent Modeling in Multi-Agent Systems

arXiv.org Machine Learning

An MDP consists of the set of states S, the set of actions A, the transition function, P (s null s, a), which is the probability of the next state, s null, given the current state, s, and the action, a, and the reward function, r ( s null, a, s), that returns a scalar value conditioned on two consecutive states and the intermediate action. A policy function is used to choose an action given a state, which can be stochastic a π (a s) or deterministic a µ(s). Given a policy π, the state value function is defined as V ( s t) E π[null H i t γ i t r t s s t] and the state-action value (Q-value) Q(s t, a t) E π[ null H i t γ i t r t s s t,a a t], where 0 γ 1 is the discount factor and H is the finite horizon of the episode. The goal of RL is to compute the policy that maximizes state value function V, when the transition and the reward functions are unknown. There is a large number of RL algorithms; however, in this work, we focus on two actor-critic algorithms; the synchronous Advantage Actor-Critic (A2C) [Mnih et al., 2016, Dhariwal et al., 2017] and the Deep Deterministic Policy Gradient (DDPG) [Silver et al., 2014, Lillicrap et al., 2015]. DDPG is an off-policy algorithm, using an experience replay for breaking the correlation between consecutive samples and target networks for stabilizing the training [Mnih et al., 2015]. Given an actor network with parameters θ and a critic network with parameter φ, the gradient updates are performed using the following update rules.


Artificial Intelligence In Video Games - Latest, Trending Automation News

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We have seen many applications of AI in industry sectors but in regards to the entertainment niche, Artificial Intelligence continues to diversify day by day operations. Video Games are an essential part of almost every person out there, after a tiring day of work, we all relax with a video game whether it is on our handheld device or dedicated gaming rig. The goal of Artificial Intelligence in video games is not limited to competitive gameplay but rather it intends to create the most enjoyable AI for players to compete with. The inclusion of AI in games dates back to the early 90s when Wolfstein 3D came out but the AI non-player character (NPC) was at the beginning level at that time. It implemented simple concepts such as to evade the player's attacks and attack when the appropriate time came, all in an enclosed finite state machine.


Inducing Cooperation in Multi-Agent Games Through Status-Quo Loss

arXiv.org Artificial Intelligence

Social dilemma situations bring out the conflict between individual and group rationality. When individuals act rationally in such situations, the group suffers sub-optimal outcomes. The Iterative Prisoner's Dilemma (IPD) is a two-player game that offers a theoretical framework to model and study such social situations. In the Prisoner's Dilemma, individualistic behavior leads to mutual defection and sub-optimal outcomes. This result is in contrast to what one observes in human groups, where humans often sacrifice individualistic behavior for the good of the collective. It is interesting to study how and why such cooperative and individually irrational behavior emerges in human groups. To this end, recent work models this problem by treating each player as a Deep Reinforcement Learning (RL) agent and evolves cooperative behavioral policies through internal information or reward sharing mechanisms. We propose an approach to evolve cooperative behavior between RL agents playing the IPD game without sharing rewards, internal details (weights, gradients), or a communication channel. We introduce a Status-Quo loss (SQLoss) that incentivizes cooperative behavior by encouraging policy stationarity. We also describe an approach to transform a two-player game (with visual inputs) into its IPD formulation through self-supervised skill discovery (IPDistill).We show how our approach outperforms existing approaches in the Iterative Prisoner's Dilemma and the two-player Coin game.


victorqribeiro/bangBangML

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Watch a Neural Network learns how to shoot a target. Halfway through creating a clone of the classic windows game Bang Bang I realized I need a interesting Artificial Intelligence to play against the player. So I thought about having the opponent cannon be controlled by a Neural Network and learn how to shoot during run time. I came up with this algorithm to train the Neural Network. On this step I'm not saving the training data, as I don't care for a miss shot.