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Deep Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/clementbernardd/Count-Based-Exploration
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
https://github.com/clinplayer/3DModelingRL
https://github.com/clrrrr/promp_plus
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/clvrai/leaps
Learning to Synthesize Programs as Interpretable and Generalizable Policies
https://github.com/cocolico14/N-step-Dueling-DDQN-PER-Pacman
https://github.com/code-implementation1/Code7/tree/main/RNA
https://github.com/coderatwork7/attack
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
https://github.com/cogsys-tuebingen/uninas
https://github.com/colingwuyu/HedgingBox
Acme: A Research Framework for Distributed Reinforcement Learning
https://github.com/collinarnett/safex_trading_bot
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/collinarnett/xcalibra_trading_bot
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/core-robotics-lab/icct
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/coreylowman/ragz
https://github.com/coreylowman/rl_simply
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/coreylowman/synthesis
https://github.com/cove9988/TradingGym
https://github.com/cpark321/uncertainty-deep-learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/cpwan/rlor
RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Research
https://github.com/cranial-xix/famo
https://github.com/ctallec/continuous-rl
Making Deep Q-learning methods robust to time discretization
https://github.com/cuijiaxun/MITC-Project
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/cww97/visual-language-grasping
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
https://github.com/cyanrain7/trpo-in-marl
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/cyanrain7/trust-region-policy-optimisation-in-multi-agent-reinforcement-learning
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/cycraig/gym-goal
Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces
https://github.com/cycraig/gym-platform
Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces
https://github.com/cyoon1729/Multi-agent-reinforcement-learning
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/d-eremeev/ADM-VRP
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
https://github.com/dajiangsuo/EV_mixtedAutonomy
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/dan-lennox/ml-udacity-quadcopter-rl
https://github.com/danielpolimac/Ispit_Inteligentni_Agenti
https://github.com/danielytan/doom_DQN
https://github.com/danilonumeroso/MEG
https://github.com/dannysdeng/dqn-pytorch
https://github.com/darkrush/meta_learn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.