RZ-Q

RZ-Q's Stars

• Significant-Gravitas/AutoGPT

  AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

  Language:Python166k1.6k2.6k44.1k

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python132k1.1k15.7k26.3k

• mistralai/mistral-src

  Reference implementation of Mistral AI 7B v0.1 model.

  Language:Jupyter Notebook8.8k116115761

• 01-ai/Yi

  A series of large language models trained from scratch by developers @01-ai

  Language:Jupyter Notebook7.6k108291468

• Blizzard/s2client-proto

  StarCraft II Client - protocol definitions used to communicate with StarCraft II.

  Language:Python3.8k196124430

• google-research/football

  Check out the new game server:

  Language:Python3.3k943181.3k

• eureka-research/Eureka

  Official Repository for "Eureka: Human-Level Reward Design via Coding Large Language Models" (ICLR 2024)

  Language:Jupyter Notebook2.8k2537249

• Farama-Foundation/PettingZoo

  An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities

  Language:Python2.5k19369406

• openai/multiagent-particle-envs

  Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

  Language:Python2.3k17782785

• Farama-Foundation/Minigrid

  Simple and easily configurable grid world environments for reinforcement learning

  Language:Python2.1k39188602

• openai/multi-agent-emergence-environments

  Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"

  Language:Python1.6k18731302

• openai/maddpg

  Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

  Language:Python1.6k15167485

• TradeMaster-NTU/TradeMaster

  TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow:

  Language:Jupyter Notebook1.3k3966268

• THUDM/ImageReward

  [NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences for Text-to-image Generation

  Language:Python1.1k148460

• facebookresearch/mbrl-lib

  Library for Model Based RL

  Language:Python9522567154

• PKU-MARL/DexterousHands

  This is a library that provides dual dexterous hand manipulation tasks through Isaac Gym

  Language:Python619134170

• BlackSamorez/tensor_parallel

  Automatically split your PyTorch models on multiple GPUs for training & inference

  Language:Python61486638

• starry-sky6688/MADDPG

  Pytorch implementation of the MARL algorithm, MADDPG, which correspondings to the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments".

  Language:Python51454180

• marlbenchmark/off-policy

  PyTorch implementations of popular off-policy multi-agent reinforcement learning algorithms, including QMix, VDN, MADDPG, and MATD3.

  Language:Python38731267

• shacklettbp/madrona

  Language:C++30091130

• SilenceEagle/paper_downloader

  Download papers and supplemental materials from open-access paper website, such as AAAI, AISTATS, COLT, CORL, CVPR, ECCV, ICCV, ICLR, ICML, IJCAI, JMLR, NIPS, RSS, WACV.

  Language:Python2295531

• oxwhirl/smacv2

  Language:Python19553229

• cyanrain7/TRPO-in-MARL

  Language:Python18031948

• oxwhirl/wqmix

  Code for Weighted QMIX

  Language:Python1194734

• TonghanWang/DOP

  Codes accompanying the paper "DOP: Off-Policy Multi-Agent Decomposed Policy Gradients" (ICLR 2021, https://arxiv.org/abs/2007.12322)

  Language:Python512716

• bic4907/Overcooked-AI

  Offline Multi-Agent Reinforcement Learning Implementations: Solving Overcooked Game with Data-Driven Method

  Language:Python31224

• TonghanWang/CASEC-MACO-benchmark

  Codes accompanying the paper "Context-Aware Sparse Deep Coordination Graphs (https://arxiv.org/abs/2106.02886).

  Language:Python14124

• MarcDV1999/overcooked-explainability

  Application of a RL explainability method based on the construction of a Policy Graph that represents the agent's behaviour in a multi-agent RL cooperative environment (Overcooked)

  Language:Python4201

• RZ-Q/MARLHF

  Multi-agent RLHF/PbRL

  Language:Python40

• sgzZ123/GRE

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