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Deep Reinforcement Learning

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코드

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

https://github.com/sx-zhang/hoz

공식 코드

Hierarchical Object-to-Zone Graph for Object Navigation

https://github.com/sygogo/alignment_beamlines_rl

공식 코드

Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines

https://github.com/synthetic-automated-systems/ruder_mbot_rl

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Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning

https://github.com/t3as/mad-arl

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Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies

https://github.com/takuseno/d3rlpy

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d3rlpy: An Offline Deep Reinforcement Learning Library

https://github.com/tanmayshankar/RCNN_MDP

공식 코드

Reinforcement Learning via Recurrent Convolutional Neural Networks

https://github.com/taochenshh/hcp

공식 코드

Hardware Conditioned Policies for Multi-Robot Transfer Learning

https://github.com/tartrl/tikick

공식 코드

TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations

https://github.com/tavlab-iiitd/VacSIM

공식 코드

VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning

https://github.com/tedmoskovitz/DOPE

공식 코드

Tactical Optimism and Pessimism for Deep Reinforcement Learning

https://github.com/tedmoskovitz/TOP

공식 코드

Tactical Optimism and Pessimism for Deep Reinforcement Learning

https://github.com/tegg89/magnet

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Deep Multi-Agent Reinforcement Learning with Relevance Graphs

https://github.com/tejas-deo/safe-navigation-training-autonomous-vehicles-using-deep-reinforcement-learning-in-carla

공식 코드

Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA

https://github.com/tencent-ailab/rlogist

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RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning

https://github.com/tensorlayer/RLzoo

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Efficient Reinforcement Learning Development with RLzoo

https://github.com/tesfayz/ccm_madrl_mec

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Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing

https://github.com/tessavdheiden/SCR

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Social Navigation with Human Empowerment driven Deep Reinforcement Learning

https://github.com/tesslerc/ActionRobustRL

공식 코드

Action Robust Reinforcement Learning and Applications in Continuous Control

https://github.com/tgangwani/GuidanceRewards

공식 코드

Learning Guidance Rewards with Trajectory-space Smoothing

https://github.com/theilem/uavSim

공식 코드

UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning

https://github.com/thinklab-sjtu/rgm

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Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching

https://github.com/thkreutz/crowdorchestrationsim

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Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics

https://github.com/thomasychen/LOTaD

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Learning Symbolic Task Decompositions for Multi-Agent Teams

https://github.com/thu-ml/tianshou

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Tianshou: a Highly Modularized Deep Reinforcement Learning Library

https://github.com/thxsxth/POMDP_RLSepsis

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Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment

https://github.com/tianheyu927/PCGrad

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Gradient Surgery for Multi-Task Learning

https://github.com/tianhongdai/div-hindsight

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Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay

https://github.com/tianyady/fedslate

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FedSlate:A Federated Deep Reinforcement Learning Recommender System

https://github.com/tianyuan1wang/seqanglerl

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Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning

https://github.com/tiencapham/ife

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Pay Attention to What and Where? Interpretable Feature Extractor in Vision-based Deep Reinforcement Learning

https://github.com/tilmanimmisch/gwrr

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Map-based Experience Replay: A Memory-Efficient Solution to Catastrophic Forgetting in Reinforcement Learning

https://github.com/timotheemathieu/adaptive_stopping_mc_rl

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AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents

https://github.com/tirthankar95/numberlearninginchildren_rl_nlp

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Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning

https://github.com/tmdt-buw/schlably

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schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments

https://github.com/tobiaslv/rad

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Conformal Symplectic Optimization for Stable Reinforcement Learning

https://github.com/tombewley/TripleTree

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TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments

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