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

https://github.com/e155763/GAN-RL

Surprising Negative Results for Generative Adversarial Tree Search

https://github.com/eac-replication/eac-replication

Accelerated Methods for Deep Reinforcement Learning

https://github.com/eddynelson/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/edmundmills/basalt-competition

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/efg59/Rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/eladsar/rbi

Proximal Policy Optimization Algorithms

https://github.com/emerge-lab/nocturne_lab

Proximal Policy Optimization Algorithms

https://github.com/emiled16/Beyond_prioritized_experience_replay

Prioritized Experience Replay

https://github.com/enajx/ES

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/enpasos/muzero

Online and Offline Reinforcement Learning by Planning with a Learned Model

https://github.com/eparisotto/ActorMimic

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

https://github.com/epignatelli/human-level-control-through-deep-reinforcement-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ercumentilhan/MinAtar

MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

https://github.com/eric-yim/fin_map

A Distributional Perspective on Reinforcement Learning

https://github.com/ericborn/binarybot

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/ermongroup/higher_order_invariance

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/esceptico/perceiver-io

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/esmeralday/MARL

Playing Atari with Deep Reinforcement Learning

https://github.com/ethanmclark1/carla_aebs

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/ethz-asl/background_foreground_segmentation

Overcoming catastrophic forgetting in neural networks

https://github.com/eublefar/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/evaboost/evaboost

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/evieq01/oodil

Proximal Policy Optimization Algorithms

https://github.com/fabiopardo/qmap

Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks

https://github.com/facebookarchive/MazeBase

MazeBase: A Sandbox for Learning from Games

https://github.com/facebookresearch/Horizon

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/facebookresearch/ReAgent

Deep Reinforcement Learning with Double Q-learning

https://github.com/facebookresearch/benchmarl

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

https://github.com/facebookresearch/gala

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

https://github.com/facebookresearch/minihack

A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

https://github.com/facebookresearch/rl/blob/main/examples/dqn/dqn.py

Playing Atari with Deep Reinforcement Learning

https://github.com/facebookresearch/salina

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/facebookresearch/torchbeast

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

https://github.com/fapont/hackaton-hiparis-2021

Momentum-based Accelerated Q-learning

https://github.com/farama-foundation/microrts-py

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

https://github.com/farama-foundation/vizdoom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

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