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Minecraft

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Alescontrela/viper_rl

Mastering Diverse Domains through World Models

https://github.com/AsWali/MineAgentFinal

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

https://github.com/RyanNavillus/Syllabus

Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft

https://github.com/Shalev-Lifshitz/STEVE-1

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft

https://github.com/alescontrela/viper

Mastering Diverse Domains through World Models

https://github.com/banben/craftassist_mod

CraftAssist: A Framework for Dialogue-enabled Interactive Agents

https://github.com/bfmat/agenteval

Evaluating Agents without Rewards

https://github.com/danijar/daydreamer

Mastering Diverse Domains through World Models

https://github.com/deckardagent/deckard

Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling

https://github.com/facebookresearch/droidlet

CraftAssist: A Framework for Dialogue-enabled Interactive Agents

https://github.com/glambrechts/informed-dreamer

Mastering Diverse Domains through World Models

https://github.com/iglu-contest/nlp-baselines-2022

Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions

https://github.com/juliuskunze/cwvae-jax

Clockwork Variational Autoencoders

https://github.com/kimbring2/DQFD_Minecraft

Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft

https://github.com/lcswillems/automatic-curriculum

Teacher-Student Curriculum Learning

https://github.com/metataro/minerl_agent

Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft

https://github.com/minerllabs/minerl

MineRL: A Large-Scale Dataset of Minecraft Demonstrations

https://github.com/mkunesch/remainder-rnn

Teacher-Student Curriculum Learning

https://github.com/nm512/dreamerv3-torch

Mastering Diverse Domains through World Models

https://github.com/nvlabs/convssm

Convolutional State Space Models for Long-Range Spatiotemporal Modeling

https://github.com/opendilab/DI-engine

Mastering Diverse Domains through World Models

https://github.com/openlamm/lamm

MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception

https://github.com/peterhj/arm-pytorch

Regret Minimization for Partially Observable Deep Reinforcement Learning

https://github.com/pku-rl/copl

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

https://github.com/rishavb123/MineRL

Deep Recurrent Q-Learning vs Deep Q-Learning on a simple Partially Observable Markov Decision Process with Minecraft

https://github.com/rolv-arild/necto

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

https://github.com/salesforce/sibling-rivalry

Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards

https://github.com/shadowyzy/NIPS2019-MineRL-Competition-solution

The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

https://github.com/showlab/FAR

Temporally Consistent Transformers for Video Generation

https://github.com/sled-group/MindCraft

MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks

https://github.com/tufts-ai-robotics-group/polycraft-novelty-detection

NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds

https://github.com/wattlebirdaz/geql

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains

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