Learning to Play Video Games with Intuitive Physics Priors

• URL: http://arxiv.org/abs/2409.13886v1
• Date: Fri, 20 Sep 2024 20:30:27 GMT
• Title: Learning to Play Video Games with Intuitive Physics Priors
• Authors: Abhishek Jaiswal, Nisheeth Srivastava,
• Abstract summary: We design object-based input representations that generalize well across a number of video games. Using these representations, we evaluate an agent's ability to learn games similar to an infant. Our results suggest that a human-like object interaction setup capably learns to play several video games.
• Score: 2.1548132286330453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video game playing is an extremely structured domain where algorithmic decision-making can be tested without adverse real-world consequences. While prevailing methods rely on image inputs to avoid the problem of hand-crafting state space representations, this approach systematically diverges from the way humans actually learn to play games. In this paper, we design object-based input representations that generalize well across a number of video games. Using these representations, we evaluate an agent's ability to learn games similar to an infant - with limited world experience, employing simple inductive biases derived from intuitive representations of physics from the real world. Using such biases, we construct an object category representation to be used by a Q-learning algorithm and assess how well it learns to play multiple games based on observed object affordances. Our results suggest that a human-like object interaction setup capably learns to play several video games, and demonstrates superior generalizability, particularly for unfamiliar objects. Further exploring such methods will allow machines to learn in a human-centric way, thus incorporating more human-like learning benefits.

Related papers

• Gamifying Math Education using Object Detection [0.696125353550498]
  We present a curriculum inspired teaching system for kids aged 5-8 to learn geometry using shape tile manipulatives. This introduces a challenge of oriented object detection for densely packed objects with arbitrary orientations. We enable our system to understand user interactions and provide real-time audiovisual feedback.
  arXiv  Detail & Related papers  (2023-04-13T05:06:33Z)
• A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation [63.1610540170754]
  We focus on the problem of visual non-prehensile planar manipulation. We propose a novel architecture that combines video decoding neural models with priors from contact mechanics. We find that our modular and fully differentiable architecture performs better than learning-only methods on unseen objects and motions.
  arXiv  Detail & Related papers  (2021-11-09T18:39:45Z)
• Playful Interactions for Representation Learning [82.59215739257104]
  We propose to use playful interactions in a self-supervised manner to learn visual representations for downstream tasks. We collect 2 hours of playful data in 19 diverse environments and use self-predictive learning to extract visual representations. Our representations generalize better than standard behavior cloning and can achieve similar performance with only half the number of required demonstrations.
  arXiv  Detail & Related papers  (2021-07-19T17:54:48Z)
• Contrastive Learning of Generalized Game Representations [2.323282558557423]
  Representing games through their pixels offers a promising approach for building general-purpose and versatile game models. While games are not merely images, neural network models trained on game pixels often capture differences of the visual style of the image rather than the content of the game. In this paper we build on recent advances in contrastive learning and showcase its benefits for representation learning in games.
  arXiv  Detail & Related papers  (2021-06-18T11:17:54Z)
• Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision tree [55.41644538483948]
  We present a new machine learning model by imitation based on the linguistic description of complex phenomena. The method can be a good alternative to design and implement the behaviour of intelligent agents in video game development.
  arXiv  Detail & Related papers  (2021-01-06T21:14:10Z)
• Learning Object Manipulation Skills via Approximate State Estimation from Real Videos [47.958512470724926]
  Humans are adept at learning new tasks by watching a few instructional videos. On the other hand, robots that learn new actions either require a lot of effort through trial and error, or use expert demonstrations that are challenging to obtain. In this paper, we explore a method that facilitates learning object manipulation skills directly from videos.
  arXiv  Detail & Related papers  (2020-11-13T08:53:47Z)
• Learning Dexterous Grasping with Object-Centric Visual Affordances [86.49357517864937]
  Dexterous robotic hands are appealing for their agility and human-like morphology. We introduce an approach for learning dexterous grasping. Our key idea is to embed an object-centric visual affordance model within a deep reinforcement learning loop.
  arXiv  Detail & Related papers  (2020-09-03T04:00:40Z)
• Benchmarking End-to-End Behavioural Cloning on Video Games [5.863352129133669]
  We study the general applicability of behavioural cloning on twelve video games, including six modern video games (published after 2010) Our results show that these agents cannot match humans in raw performance but do learn basic dynamics and rules. We also demonstrate how the quality of the data matters, and how recording data from humans is subject to a state-action mismatch, due to human reflexes.
  arXiv  Detail & Related papers  (2020-04-02T13:31:51Z)
• Exploration Based Language Learning for Text-Based Games [72.30525050367216]
  This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text. These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents.
  arXiv  Detail & Related papers  (2020-01-24T03:03:51Z)

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.