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

https://github.com/uber-research/coordconv

공식 코드

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/uber-research/go-explore

공식 코드

First return, then explore

https://github.com/ubiquition/drl

공식 코드

Joint action loss for proximal policy optimization

https://github.com/ubisoft/ubisoft-laforge-toxbuster

공식 코드

Towards Detecting Contextual Real-Time Toxicity for In-Game Chat

https://github.com/ucbtns/dai

공식 코드

Active inference: demystified and compared

https://github.com/uoe-agents/pressureplate

공식 코드

Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization

https://github.com/uoe-agents/smaclite

공식 코드

SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning

https://github.com/valeoai/rainbow-iqn-apex

공식 코드

Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field

https://github.com/vwxyzjn/action-guidance

공식 코드

Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games

https://github.com/vwxyzjn/gym-microrts

공식 코드

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/vwxyzjn/gym-microrts-paper

공식 코드

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/vwxyzjn/invalid-action-masking

공식 코드

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms

https://github.com/wangjksjtu/rl-perturbed-reward

공식 코드

Reinforcement Learning with Perturbed Rewards

https://github.com/wendelinboehmer/dcg

공식 코드

Deep Coordination Graphs

https://github.com/werner-duvaud/muzero-general

공식 코드

Mastering Atari Games with Limited Data

https://github.com/wjh720/QPLEX

공식 코드

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/wponghiran/lsm-rl

공식 코드

Reinforcement Learning with Low-Complexity Liquid State Machines

https://github.com/wudiymy/dqmix

공식 코드

MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning

https://github.com/wuhuikai/MSC

공식 코드

MSC: A Dataset for Macro-Management in StarCraft II

https://github.com/xingruiyu/GIRIL

공식 코드

Intrinsic Reward Driven Imitation Learning via Generative Model

https://github.com/xinjinghao/color

공식 코드

Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity

https://github.com/yewr/efficientzero

공식 코드

Mastering Atari Games with Limited Data

https://github.com/yiqinyang/icq

공식 코드

Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning

https://github.com/youpengzhao/crashed_agent

공식 코드

Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents

https://github.com/ysmei97/mac-po

공식 코드

MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization

https://github.com/yudasong/hyq

공식 코드

Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

https://github.com/zhang-shaowei/smmae

공식 코드

Self-Motivated Multi-Agent Exploration

https://github.com/zhao-song/Softmax-DQN

공식 코드

Revisiting the Softmax Bellman Operator: New Benefits and New Perspective

https://github.com/zhengyinan-air/omiga

공식 코드

Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization

https://github.com/zyh1999/cadp

공식 코드

Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?

https://bitbucket.org/act-lab/release

Proximal Policy Optimization Algorithms

https://github.com/1230113202/NV-JM-DD

The Natural Language of Actions

https://github.com/15534081591/QMIX

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/16444take/aope-sim

Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

https://github.com/170928/-Review-Dueling-Deep-Q-Network

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/170928/-Review-Generative-Adversarial-Imitation-Learning

Trust Region Policy Optimization

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.