- Hanam-si, Gyeonggi-do, Republic of Korea
- dongyeongkim33@gmail.com
- @Dongyeongkim3
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A JAX-based simulator for autonomous driving research.
Dataset to assess the disentanglement properties of unsupervised learning methods
🏛️A research-friendly codebase for fast experimentation of single-agent reinforcement learning in JAX • End-to-End JAX RL
DreamerV3 implementation of Curious Replay, a method for prioritizing experience replay that is tailored to model-based reinforcement learning agents.
megastep helps you build 1-million FPS reinforcement learning environments on a single GPU
TensorFlow's Visualization Toolkit
🕹️ A diverse suite of scalable reinforcement learning environments in JAX
An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
🤗 LeRobot: End-to-end Learning for Real-World Robotics in Pytorch
PIX is an image processing library in JAX, for JAX.
JAX-accelerated Meta-Reinforcement Learning Environments Inspired by XLand and MiniGrid 🏎️
A collection of high-quality models for the MuJoCo physics engine, curated by Google DeepMind.
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
[ICLR 2023] Choreographer: a model-based agent that discovers and learns unsupervised skills in latent imagination, and it's able to efficiently coordinate and adapt the skills to solve downstream …
chaihahaha / jaxrenderer
Forked from JoeyTeng/jaxrendererDifferentiable Rasteriser implemented in JAX. Reference: https://github.com/erwincoumans/tinyrenderer, https://github.com/ssloy/tinyrenderer/wiki; PR: https://github.com/google/brax/pull/367
Differentiable Rasteriser implemented in JAX. Reference: https://github.com/erwincoumans/tinyrenderer, https://github.com/ssloy/tinyrenderer/wiki; PR: https://github.com/google/brax/pull/367
A massively parallel, high-level programming language
Evaluation Code repository for the paper "ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers". (2023 TMLR Submission)
Finetuning Large Language Models on One Consumer GPU in Under 4 Bits
An open-source reinforcement learning environment build toolkit for ROS and Gazebo.