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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/GuetYe/DRL-M4MR

공식 코드

DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN

https://github.com/GuoLanqing/ReLLIE

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ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement

https://github.com/HDFengChina/How-to-Control-Hydrodynamic-Force

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How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning

https://github.com/HIT-SMC/DRL_solver

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General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning

https://github.com/Hananel-Hazan/bindsnet

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Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

https://github.com/HarrieO/RankingComplexLayouts

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Ranking for Relevance and Display Preferences in Complex Presentation Layouts

https://github.com/Hisato-Komatsu/DLS-DDPG

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Application of linear regression method to the deep reinforcement learning in continuous action cases

https://github.com/HjalmarWijk/bounded-prescience

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Shielding Atari Games with Bounded Prescience

https://github.com/HumanCompatibleAI/adversarial-policies

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Adversarial Policies: Attacking Deep Reinforcement Learning

https://github.com/HumanCompatibleAI/atari-irl

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Inverse reinforcement learning for video games

https://github.com/IBM/TRAIL

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A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving

https://github.com/IGITUGraz/spore-nest-module

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Embodied Synaptic Plasticity with Online Reinforcement learning

https://github.com/ImanRHT/QECO

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QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing

https://github.com/IndustAI/risk-and-uncertainty

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Estimating Risk and Uncertainty in Deep Reinforcement Learning

https://github.com/InsaneMonster/telerl2021

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Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic

https://github.com/IntelLabs/DNS

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DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning

https://github.com/Intelligent-Systems-Research-Group/RL4MicrostructureEvolution

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Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization

https://github.com/IoannisKansizoglou/Deep-Feature-Space

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Deep Feature Space: A Geometrical Perspective

https://github.com/IouJenLiu/HTS-RL

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High-Throughput Synchronous Deep RL

https://github.com/IouJenLiu/PIC

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PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning

https://github.com/IsmaelTito/SEC

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Sequential memory improves sample and memory efficiency in Episodic Control

https://github.com/JBLanier/pipeline-psro

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Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

https://github.com/JHU-APL-ISC-Deep-RL/risk-sensitive

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A Risk-Sensitive Approach to Policy Optimization

https://github.com/JacobPfau/procgenAISC

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Goal Misgeneralization in Deep Reinforcement Learning

https://github.com/Jacobkooi/InclinedDroneLander

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Inclined Quadrotor Landing using Deep Reinforcement Learning

https://github.com/JanMatas/Rainbow_ddpg

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Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

https://github.com/JiahuiYu/slimmable_networks

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Universally Slimmable Networks and Improved Training Techniques

https://github.com/JieRen98/rlkit-pmoe

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Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

https://github.com/JingyuanZhou/Safe_RL_Mixed_Traffic

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Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning

https://github.com/JohanSamir/revisiting_rainbow

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Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research

https://github.com/JoyZhang20/SRCL_Simu5G

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

https://github.com/K-Coconut/MCPBenchmark

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A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs

https://github.com/KDL-umass/saliency_maps

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Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning

https://github.com/KaiyangZhou/vsumm-reinforce

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

https://github.com/KarlXing/LUSR

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Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation

https://github.com/KohlerHECTOR/DPDTreeEstimator

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Interpretable Decision Tree Search as a Markov Decision Process

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