Predicting Game Engagement and Difficulty Using AI Players

• URL: http://arxiv.org/abs/2107.12061v1
• Date: Mon, 26 Jul 2021 09:31:57 GMT
• Title: Predicting Game Engagement and Difficulty Using AI Players
• Authors: Shaghayegh Roohi and Christian Guckelsberger and Asko Relas and Henri Heiskanen and Jari Takatalo and Perttu H\"am\"al\"ainen
• Abstract summary: This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. It has previously been demonstrated that Deep Reinforcement Learning game-playing agents can predict both game difficulty and player engagement. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS)
• Score: 3.0501851690100277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. It has previously been demonstrated that Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty and player engagement, operationalized as average pass and churn rates. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS). We also motivate an enhanced selection strategy for predictor features, based on the observation that an AI agent's best-case performance can yield stronger correlations with human data than the agent's average performance. Both additions consistently improve the prediction accuracy, and the DRL-enhanced MCTS outperforms both DRL and vanilla MCTS in the hardest levels. We conclude that player modelling via automated playtesting can benefit from combining DRL and MCTS. Moreover, it can be worthwhile to investigate a subset of repeated best AI agent runs, if AI gameplay does not yield good predictions on average.

Related papers

• Reinforcing Competitive Multi-Agents for Playing So Long Sucker [0.393259574660092]
  This paper examines the use of classical deep reinforcement learning (DRL) algorithms, DQN, DDQN, and Dueling DQN, in the strategy game So Long Sucker. The study's primary goal is to teach autonomous agents the game's rules and strategies using classical DRL methods.
  arXiv  Detail & Related papers  (2024-11-17T12:38:13Z)
• Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining [49.730897226510095]
  We introduce JOWA: Jointly-Reinforced World-Action model, an offline model-based RL agent pretrained on Atari games with 6 billion tokens data. Our largest agent, with 150 million parameters, 78.9% human-level performance on pretrained games using only 10% subsampled offline data, outperforming existing state-of-the-art large-scale offline RL baselines by 31.6% on averange.
  arXiv  Detail & Related papers  (2024-10-01T10:25:03Z)
• Toward Optimal LLM Alignments Using Two-Player Games [86.39338084862324]
  In this paper, we investigate alignment through the lens of two-agent games, involving iterative interactions between an adversarial and a defensive agent. We theoretically demonstrate that this iterative reinforcement learning optimization converges to a Nash Equilibrium for the game induced by the agents. Experimental results in safety scenarios demonstrate that learning in such a competitive environment not only fully trains agents but also leads to policies with enhanced generalization capabilities for both adversarial and defensive agents.
  arXiv  Detail & Related papers  (2024-06-16T15:24:50Z)
• Behavioural Cloning in VizDoom [1.4999444543328293]
  This paper describes methods for training autonomous agents to play the game "Doom 2" through Imitation Learning (IL) We also explore how Reinforcement Learning (RL) compares to IL for humanness by comparing camera movement and trajectory data.
  arXiv  Detail & Related papers  (2024-01-08T16:15:43Z)
• Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play [12.754819077905061]
  Minimax Exploiter is a game theoretic approach to exploiting Main Agents that leverages knowledge of its opponents. We validate our approach in a diversity of settings, including simple turn based games, the arcade learning environment, and For Honor, a modern video game.
  arXiv  Detail & Related papers  (2023-11-28T19:34:40Z)
• Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning [95.78031053296513]
  No-press Diplomacy is a complex strategy game involving both cooperation and competition. We introduce a planning algorithm we call DiL-piKL that regularizes a reward-maximizing policy toward a human imitation-learned policy. We show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL.
  arXiv  Detail & Related papers  (2022-10-11T14:47:35Z)
• Retrieval-Augmented Reinforcement Learning [63.32076191982944]
  We train a network to map a dataset of past experiences to optimal behavior. The retrieval process is trained to retrieve information from the dataset that may be useful in the current context. We show that retrieval-augmented R2D2 learns significantly faster than the baseline R2D2 agent and achieves higher scores.
  arXiv  Detail & Related papers  (2022-02-17T02:44:05Z)
• TiKick: Toward Playing Multi-agent Football Full Games from Single-agent Demonstrations [31.596018856092513]
  Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game. To the best of our knowledge, Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game.
  arXiv  Detail & Related papers  (2021-10-09T08:34:58Z)
• Multi-Agent Collaboration via Reward Attribution Decomposition [75.36911959491228]
  We propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge. CollaQ is evaluated on various StarCraft Attribution maps and shows that it outperforms existing state-of-the-art techniques.
  arXiv  Detail & Related papers  (2020-10-16T17:42:11Z)
• Predicting Game Difficulty and Churn Without Players [0.0]
  We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast. Our work demonstrates that player behavior predictions produced by DRL gameplay can be significantly improved by even a very simple population-level simulation of individual player differences.
  arXiv  Detail & Related papers  (2020-08-29T08:37:47Z)
• Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork [54.309495231017344]
  We argue that AI systems should be trained in a human-centered manner, directly optimized for team performance. We study this proposal for a specific type of human-AI teaming, where the human overseer chooses to either accept the AI recommendation or solve the task themselves. Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance.
  arXiv  Detail & Related papers  (2020-04-27T19:06:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.