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

https://github.com/tlohr/nfsu2-ai

Playing Atari with Deep Reinforcement Learning

https://github.com/tmjeong1103/RL_with_RAY

Proximal Policy Optimization Algorithms

https://github.com/tobiasemrich/SchafkopfRL

Proximal Policy Optimization Algorithms

https://github.com/tommyvsfu1/RL-NTU

Proximal Policy Optimization Algorithms

https://github.com/toni-sm/skrl

Playing Atari with Deep Reinforcement Learning

https://github.com/tophatraptor/si-transfer

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

https://github.com/toshikwa/soft-actor-critic.pytorch

Prioritized Experience Replay

https://github.com/toybox-rs/Toybox

ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents

https://github.com/toybox-rs/openai-baselines-envs

ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents

https://github.com/tphanson/xupr-drl

Prioritized Experience Replay

https://github.com/traai/async-deep-rl

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/transedward/pytorch-hdqn

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

https://github.com/tuanpnm99/RLPongAgent

Proximal Policy Optimization Algorithms

https://github.com/tumcps/commonpower

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

https://github.com/tuomaso/SC2LE-implementation

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/typoverflow/OfflineRL-Lib

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/uam-biometrics/Transformer-Networks

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/uber-research/ape-x

Distributed Prioritized Experience Replay

https://github.com/ucl-dark/pax

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

https://github.com/ugo-nama-kun/DQN-chainer

Playing Atari with Deep Reinforcement Learning

https://github.com/ulstu/ml

Deep Attention Recurrent Q-Network

https://github.com/ulstu/robotics_ml

Deep Attention Recurrent Q-Network

https://github.com/umersheikh846/WQMIX

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

https://github.com/universome/stylegan-v

Towards Accurate Generative Models of Video: A New Metric & Challenges

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

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

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

The StarCraft Multi-Agent Challenge

https://github.com/urw7rs/spiralpp

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/utarumo/RL_implementation

Deep Reinforcement Learning with Double Q-learning

https://github.com/uvipen/Super-mario-bros-A3C-pytorch

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/uvipen/super-mario-bros-ppo-pytorch

Proximal Policy Optimization Algorithms

https://github.com/vcadillog/PPO-Mario-Bros-Tensorflow-2

Proximal Policy Optimization Algorithms

https://github.com/vcharvet/project-rl

Deep reinforcement learning from human preferences

https://github.com/vdean/audio-curiosity

Large-Scale Study of Curiosity-Driven Learning

https://github.com/vermashresth/damage-aware-PPO

Proximal Policy Optimization Algorithms

https://github.com/vheuthe/microbot_rl

Proximal Policy Optimization Algorithms

https://github.com/vicsesi/PyTorch-STN

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

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