Scaling Imitation Learning in Minecraft

• URL: http://arxiv.org/abs/2007.02701v1
• Date: Mon, 6 Jul 2020 12:47:01 GMT
• Title: Scaling Imitation Learning in Minecraft
• Authors: Artemij Amiranashvili, Nicolai Dorka, Wolfram Burgard, Vladlen Koltun, Thomas Brox
• Abstract summary: We apply imitation learning to attain state-of-the-art performance on hard exploration problems in the Minecraft environment. An early version of our approach reached second place in the MineRL competition at NeurIPS 2019.
• Score: 114.6964571273486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imitation learning is a powerful family of techniques for learning sensorimotor coordination in immersive environments. We apply imitation learning to attain state-of-the-art performance on hard exploration problems in the Minecraft environment. We report experiments that highlight the influence of network architecture, loss function, and data augmentation. An early version of our approach reached second place in the MineRL competition at NeurIPS 2019. Here we report stronger results that can be used as a starting point for future competition entries and related research. Our code is available at https://github.com/amiranas/minerl_imitation_learning.

Related papers

• Exploring Text-to-Motion Generation with Human Preference [59.28730218998923]
  This paper presents an exploration of preference learning in text-to-motion generation. We find that current improvements in text-to-motion generation still rely on datasets requiring expert labelers with motion capture systems. We show that preference learning has the potential to greatly improve current text-to-motion generative models.
  arXiv  Detail & Related papers  (2024-04-15T04:14:42Z)
• Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning [4.067733179628694]
  Craftax is a ground-up rewrite of Crafter in JAX that runs up to 250x faster than the Python-native original. A run of PPO using 1 billion environment interactions finishes in under an hour using only a single GPU. We show that existing methods including global and episodic exploration, as well as unsupervised environment design fail to make material progress on the benchmark.
  arXiv  Detail & Related papers  (2024-02-26T18:19:07Z)
• Evolving Knowledge Mining for Class Incremental Segmentation [113.59611699693092]
  Class Incremental Semantic (CISS) has been a trend recently due to its great significance in real-world applications. We propose a novel method, Evolving kNowleDge minING, employing a frozen backbone. We evaluate our method on two widely used benchmarks and consistently demonstrate new state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-06-03T07:03:15Z)
• Open-Ended Reinforcement Learning with Neural Reward Functions [2.4366811507669115]
  In high-dimensional robotic environments our approach learns a wide range of interesting skills including front-flips for Half-Cheetah and one-legged running for Humanoid. In the pixel-based Montezuma's Revenge environment our method also works with minimal changes and it learns complex skills that involve interacting with items and visiting diverse locations.
  arXiv  Detail & Related papers  (2022-02-16T15:55:22Z)
• JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning [13.57305458734617]
  We propose JueWu-MC, a sample-efficient hierarchical RL approach equipped with representation learning and imitation learning to deal with perception and exploration. Specifically, our approach includes two levels of hierarchy, where the high-level controller learns a policy to control over options and the low-level workers learn to solve each sub-task. To boost the learning of sub-tasks, we propose a combination of techniques including 1) action-aware representation learning which captures underlying relations between action and representation, 2) discriminator-based self-imitation learning for efficient exploration, and 3) ensemble behavior cloning with consistency filtering for
  arXiv  Detail & Related papers  (2021-12-07T09:24:49Z)
• CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning [58.107679606345165]
  Noisy label learning, semi-supervised learning, and contrastive learning are three different strategies for designing learning processes requiring less annotation cost. We propose CSSL, a unified Contrastive Semi-Supervised Learning algorithm, and CoDiM, a novel algorithm for learning with noisy labels.
  arXiv  Detail & Related papers  (2021-11-23T04:56:40Z)
• Incremental Class Learning using Variational Autoencoders with Similarity Learning [0.0]
  Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Our research investigates catastrophic forgetting for four well-known metric-based loss functions during incremental class learning. The angular loss was least affected, followed by contrastive, triplet loss, and centre loss with good mining techniques.
  arXiv  Detail & Related papers  (2021-10-04T10:19:53Z)
• The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors [62.9301667732188]
  We propose a second iteration of the MineRL Competition. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment. At the end of each round, competitors submit containerized versions of their learning algorithms to the AIcrowd platform.
  arXiv  Detail & Related papers  (2021-01-26T20:32:30Z)
• LID 2020: The Learning from Imperfect Data Challenge Results [242.86700551532272]
  Learning from Imperfect Data workshop aims to inspire and facilitate the research in developing novel approaches. We organize three challenges to find the state-of-the-art approaches in weakly supervised learning setting. This technical report summarizes the highlights from the challenge.
  arXiv  Detail & Related papers  (2020-10-17T13:06:12Z)
• An Overview of Deep Learning Architectures in Few-Shot Learning Domain [0.0]
  Few-Shot Learning (also known as one-shot learning) is a sub-field of machine learning that aims to create models that can learn the desired objective with less data. We have reviewed some of the well-known deep learning-based approaches towards few-shot learning.
  arXiv  Detail & Related papers  (2020-08-12T06:58:45Z)

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.