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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/justinjfu/diagnosing_qlearning

Diagnosing Bottlenecks in Deep Q-learning Algorithms

https://github.com/justinjfu/inverse_rl

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

https://github.com/justinmaojones/starr

Prioritized Experience Replay

https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution

Proximal Policy Optimization Algorithms

https://github.com/jviquerat/pbo

Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows

https://github.com/jvking/text-games

Deep Reinforcement Learning with a Natural Language Action Space

https://github.com/jvoynow/DQN-analysis-with-2048

Deep Reinforcement Learning with Double Q-learning

https://github.com/jw1401/PPO-Tensorflow-2.0

Proximal Policy Optimization Algorithms

https://github.com/jwallnoefer/gym-teleportation

Machine learning for long-distance quantum communication

https://github.com/jwawon/unity-ml-agent-study

Unity: A General Platform for Intelligent Agents

https://github.com/jxu43/replication-mbpo

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/jxwuyi/House3D

Embodied Question Answering

https://github.com/jxwuyi/HouseNavAgent

Building Generalizable Agents with a Realistic and Rich 3D Environment

https://github.com/jyqhahah/rl_maddpg_matd3

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

https://github.com/k-lombard/CS4641_Project

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/k-lombard/Deep-Learning-Chess-AI

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/k-r-allen/residual-policy-learning

Residual Policy Learning

https://github.com/kachayev/car-racing-attention-agent

Neuroevolution of Self-Interpretable Agents

https://github.com/kachayev/gym-microrts-paper-sb3

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/kaiolae/UnityMLAgents

Unity: A General Platform for Intelligent Agents

https://github.com/kairproject/kair_algorithms_draft

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

https://github.com/kaixindelele/DRLib

Overcoming Exploration in Reinforcement Learning with Demonstrations

https://github.com/kalebbennaveed/Trajectory-Planning-for-Autonomous-Vehicles-Using-HRL

Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning

https://github.com/kalelpark/Mixture-of-Granularity-Specific-Experts-for-Fine-Grained-Categorization

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

https://github.com/kaletap/deep-cube-rl

Solving the Rubik's Cube Without Human Knowledge

https://github.com/kalifou/robotics-rl-srl

Simple random search provides a competitive approach to reinforcement learning

https://github.com/kamenbliznashki/pixel_models

PixelSNAIL: An Improved Autoregressive Generative Model

https://github.com/kangkang59812/GraphCaption

Self-critical Sequence Training for Image Captioning

https://github.com/kantneel/causal-metarl

Causal Reasoning from Meta-reinforcement Learning

https://github.com/karandesaiii/CheXNet

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

https://github.com/kargarisaac/macrpo

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

https://github.com/karishnu/tf-agents-multi-particle-envs

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

https://github.com/karthikncode/DeepRL-InformationExtraction

Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

https://github.com/karush17/emix

Maximum Mutation Reinforcement Learning for Scalable Control

https://github.com/karush17/esac

Maximum Mutation Reinforcement Learning for Scalable Control

https://github.com/kasnerz/tabgenie

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

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