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

https://github.com/camall3n/atari-MAC

Mean Actor Critic

https://github.com/cannylab/casual_overhypotheses

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/cathywu/rllab-multiagent

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/catid/never_forget

Overcoming catastrophic forgetting in neural networks

https://github.com/cbfinn/maml_rl

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/cdesilv1/sc2_ai_cdes

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/cesch97/NeuroEvolution

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/chainer/chainerrl

Trust Region Policy Optimization

https://github.com/chandar-lab/LoCA2

Mastering Atari with Discrete World Models

https://github.com/chandar-lab/RLHive

Playing Atari with Deep Reinforcement Learning

https://github.com/chandar-lab/coe

Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration

https://github.com/chauncygu/multi-agent-constrained-policy-optimisation

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

https://github.com/chengyu2/vizdoom_rl_community_canberra

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

https://github.com/cindycia/Atari-SAC-Discrete

Distributed Prioritized Experience Replay

https://github.com/cipher982/ppo-exploration

Proximal Policy Optimization Algorithms

https://github.com/clam004/intro_continual_learning

Overcoming catastrophic forgetting in neural networks

https://github.com/clarky104/carla_aebs

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/clementbernardd/Count-Based-Exploration

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

https://github.com/clwainwright/proximal_policy_optimization

Proximal Policy Optimization Algorithms

https://github.com/cocolico14/N-step-Dueling-DDQN-PER-Pacman

Deep Reinforcement Learning with Double Q-learning

https://github.com/code-cultivater/MAPPG

Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient

https://github.com/codelion/adaptive-classifier/blob/main/src/adaptive_classifier/ewc.py

Overcoming catastrophic forgetting in neural networks

https://github.com/colindbrown/columbia-deep-learning-project

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/coreylowman/rl_simply

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/corl-team/CORL

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/cove9988/TradingGym

Deep Reinforcement Learning with Double Q-learning

https://github.com/crazydonkey200/neural-symbolic-machines

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

https://github.com/cts198859/deeprl_dist

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/cts198859/deeprl_network

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/cyanrain7/trpo-in-marl

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

https://github.com/cyanrain7/trust-region-policy-optimisation-in-multi-agent-reinforcement-learning

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

https://github.com/czen88/qtrader

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/czero69/acomoeye-NN

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/dalxndr/NEURO140

Overcoming catastrophic forgetting in neural networks

https://github.com/danelee2601/rl-based-automatic-berthing

Proximal Policy Optimization Algorithms

https://github.com/danielpolimac/Ispit_Inteligentni_Agenti

Asynchronous Methods for Deep Reinforcement Learning

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