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Reinforcement Learning

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

https://github.com/qyuan7/RNN_RL_molecule

Molecular De Novo Design through Deep Reinforcement Learning

https://github.com/qyuan7/RNN_TL_molecule

Molecular De Novo Design through Deep Reinforcement Learning

https://github.com/rabihchamas/dcls-gradcam-eval

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/raccoon831012/StartCraft2-RL

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/racheljose21/chatbot

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/rahul263-stack/Self-Driving-Car

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/rahulcoding/CheXNet

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/rahulpandeycs/openspiel-games

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/rainandwind1/MADDPG-reconstruct

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/rainandwind1/MERL

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/rainandwind1/Maddpg_multiagent

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/raise-lab/soc-nav-training

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

https://github.com/raja17021998/Augmented-Random-Search-Implementation

Simple random search provides a competitive approach to reinforcement learning

https://github.com/rajahaseeb147/PCD_Registration_ReAgent

ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning

https://github.com/rajaswa/feedback-and-memory-in-transformers

Addressing Some Limitations of Transformers with Feedback Memory

https://github.com/rajeev595/RHS_HierNSE

Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach

https://github.com/rallen10/multiagent-particle-envs

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/ramanans1/dm_control

DeepMind Control Suite

https://github.com/ran-weii/cleanil

Generative Adversarial Imitation Learning

https://github.com/ran-weii/objective_mismatch_papers

Objective Mismatch in Model-based Reinforcement Learning

https://github.com/rangwani-harsh/PC_Grad_Pytorch

Gradient Surgery for Multi-Task Learning

https://github.com/raoshashank/Tennis-with-MADDPG

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/rasmusbergpalm/evostrat

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/rasmusbergpalm/hebbian-evolution

Meta-Learning through Hebbian Plasticity in Random Networks

https://github.com/ravi-lanka-4/CoPiEr

Co-training for Policy Learning

https://github.com/ray-project/ray/tree/master/rllib

Playing Atari with Deep Reinforcement Learning

https://github.com/rayanramoul/Active-Object-Localization-Deep-Reinforcement-Learning

Active Object Localization with Deep Reinforcement Learning

https://github.com/rayanramoul/GradCAM

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/raysr/Active-Object-Localization-Deep-Reinforcement-Learning

Active Object Localization with Deep Reinforcement Learning

https://github.com/rcheng805/RL-CBF

End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

https://github.com/rddy/ReQueST

Reward learning from human preferences and demonstrations in Atari

https://github.com/rddy/deepassist

Shared Autonomy via Deep Reinforcement Learning

https://github.com/reallygooday/60daysofudacity

Representation Learning with Contrastive Predictive Coding

https://github.com/rebekkaorth/robotics_pick_n_place_masterproject

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

https://github.com/rehoss/lbmpc_semimarkov

An Experimental Design Perspective on Model-Based Reinforcement Learning

https://github.com/reinforcement-learning-kr/lets-do-irl

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

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