PCGRL: Procedural Content Generation via Reinforcement Learning

• URL: http://arxiv.org/abs/2001.09212v3
• Date: Thu, 13 Aug 2020 02:31:50 GMT
• Title: PCGRL: Procedural Content Generation via Reinforcement Learning
• Authors: Ahmed Khalifa, Philip Bontrager, Sam Earle and Julian Togelius
• Abstract summary: We investigate how reinforcement learning can be used to train level-designing agents in games. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action. This approach can be used when few or no examples exist to train from, and the trained generator is very fast.
• Score: 6.32656340734423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes and apply these to three game environments.

Related papers

• Reconstructing Existing Levels through Level Inpainting [3.1788052710897707]
  This paper introduces Content Augmentation and focuses on the subproblem of level inpainting. We present two approaches for level inpainting: an Autoencoder and a U-net.
  arXiv  Detail & Related papers  (2023-09-18T04:10:27Z)
• Lode Enhancer: Level Co-creation Through Scaling [6.739485960737326]
  We explore AI-powered upscaling as a design assistance tool in the context of creating 2D game levels. Deep neural networks are used to upscale artificially downscaled patches of levels from the puzzle platformer game Lode Runner.
  arXiv  Detail & Related papers  (2023-08-03T05:23:07Z)
• Supervised Pretraining Can Learn In-Context Reinforcement Learning [96.62869749926415]
  In this paper, we study the in-context learning capabilities of transformers in decision-making problems. We introduce and study Decision-Pretrained Transformer (DPT), a supervised pretraining method where the transformer predicts an optimal action. We find that the pretrained transformer can be used to solve a range of RL problems in-context, exhibiting both exploration online and conservatism offline.
  arXiv  Detail & Related papers  (2023-06-26T17:58:50Z)
• Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content [2.7412662946127755]
  We consider the problem of procedural content generation for video game levels. Prior approaches have relied on evolutionary search (ES) methods capable of generating diverse levels. We propose a framework to tackle the procedural content generation problem that combines the best of ES and RL.
  arXiv  Detail & Related papers  (2022-07-29T16:25:52Z)
• SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection [108.57862846523858]
  We revisit the self-supervised multi-task learning framework, proposing several updates to the original method. We modernize the 3D convolutional backbone by introducing multi-head self-attention modules. In our attempt to further improve the model, we study additional self-supervised learning tasks, such as predicting segmentation maps.
  arXiv  Detail & Related papers  (2022-07-16T19:25:41Z)
• Learning Neuro-Symbolic Skills for Bilevel Planning [63.388694268198655]
  Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches, such as task and motion planning (TAMP), address these challenges by decomposing decision-making into two or more levels of abstraction. Our main contribution is a method for learning parameterized polices in combination with operators and samplers.
  arXiv  Detail & Related papers  (2022-06-21T19:01:19Z)
• Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation [118.27432851053335]
  This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track. The No Interaction track targets for learning policies from pre-collected demonstration trajectories. In this track, we design a Heuristic Rule-based Method (HRM) to trigger high-quality object manipulation by decomposing the task into a series of sub-tasks. For each sub-task, the simple rule-based controlling strategies are adopted to predict actions that can be applied to robotic arms.
  arXiv  Detail & Related papers  (2022-06-13T16:20:42Z)
• Measuring and Harnessing Transference in Multi-Task Learning [58.48659733262734]
  Multi-task learning can leverage information learned by one task to benefit the training of other tasks. We analyze the dynamics of information transfer, or transference, across tasks throughout training.
  arXiv  Detail & Related papers  (2020-10-29T08:25:43Z)
• TOAD-GAN: Coherent Style Level Generation from a Single Example [24.039037782220017]
  We present TOAD-GAN, a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes.
  arXiv  Detail & Related papers  (2020-08-04T13:44:50Z)
• Subset Sampling For Progressive Neural Network Learning [106.12874293597754]
  Progressive Neural Network Learning is a class of algorithms that incrementally construct the network's topology and optimize its parameters based on the training data. We propose to speed up this process by exploiting subsets of training data at each incremental training step. Experimental results in object, scene and face recognition problems demonstrate that the proposed approach speeds up the optimization procedure considerably.
  arXiv  Detail & Related papers  (2020-02-17T18:57:33Z)
• Learning to Generate Levels From Nothing [5.2508303190856624]
  We propose Generative Playing Networks which design levels for itself to play. The algorithm is built in two parts; an agent that learns to play game levels, and a generator that learns the distribution of playable levels. We demonstrate the capability of this framework by training an agent and level generator for a 2D dungeon crawler game.
  arXiv  Detail & Related papers  (2020-02-12T22:07:23Z)

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.