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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/Div99/IQ-Learn

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

https://github.com/DrArryYao/RL-SC

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/DrJupiter/Deep-Reinforcement-Rubiks-Cube-Solver

Revisiting Fundamentals of Experience Replay

https://github.com/DuaneNielsen/rnd

Exploration by Random Network Distillation

https://github.com/EACN2022/EACN

Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/ElisevanderPol/symmetrizer

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

https://github.com/EpiSci/SoCRATES

DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling

https://github.com/Eungyu-Jin/Dropout_uncertainty

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/EyaRhouma/collaboration-competition-MADDPG

Continuous control with deep reinforcement learning

https://github.com/FOCAL-ICLR/FOCAL-ICLR

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/FaboNo/DRLND

Deep Reinforcement Learning with Double Q-learning

https://github.com/FauzaanQureshi/deep-Q-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/Finspire13/RL-Surgical-Gesture-Segmentation

Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification

https://github.com/FlyienSHaDOw/continuous_control

Continuous control with deep reinforcement learning

https://github.com/FlyienSHaDOw/project_2_continuous_control

Continuous control with deep reinforcement learning

https://github.com/Frager/Deep-Neuroevolution-In-SC2LE

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/Francesco-Sovrano/Combining--experience-replay--with--exploration-by-random-network-distillation-

Exploration by Random Network Distillation

https://github.com/Gary-Shi/Tank

Playing Atari with Deep Reinforcement Learning

https://github.com/GeorgeDUT/MetaRLSAS

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/Giovanni3A/LearningDynamics

Neural Replicator Dynamics

https://github.com/Godofnothing/EfficientObjectDetection

Efficient Object Detection in Large Images using Deep Reinforcement Learning

https://github.com/GoingMyWay/dopamine_reward_decomposition

Dopamine: A Research Framework for Deep Reinforcement Learning

https://github.com/Gouet/DDPG_PendulumV1

Continuous control with deep reinforcement learning

https://github.com/GuanSuns/Simple-Human-in-the-Loop-ML-Interface

Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

https://github.com/GuessWhatGame/guesswhat

End-to-end optimization of goal-driven and visually grounded dialogue systems

https://github.com/HJDQN/HJQ

Continuous control with deep reinforcement learning

https://github.com/Henry1iu/ierg5350_rl_course_project

Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

https://github.com/HoganZhang/pytorch-vsumm-reinforce

Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward

https://github.com/HumanCompatibleAI/multi-agent

A multi-agent reinforcement learning model of common-pool resource appropriation

https://github.com/HussonnoisMaxence/RL_Algorithms

Deep Reinforcement Learning with Double Q-learning

https://github.com/HzcIrving/DLRL-PlayGround/tree/main/Offline%20RL/BCQ

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/IAARhub/TrucoAnalytics

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

https://github.com/IBM/automation-of-image-data-preprocessing

Automated Image Data Preprocessing with Deep Reinforcement Learning

https://github.com/IBM/vsrl-framework

Verifiably Safe Exploration for End-to-End Reinforcement Learning

https://github.com/IMAC-projects/mesh-deformation

Modeling 3D Shapes by Reinforcement Learning

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