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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/zhongdy15/npm

공식 코드

No Prior Mask: Eliminate Redundant Action for Deep Reinforcement Learning

https://github.com/zhouhao0605/signal_control_paper

공식 코드

Congested Urban Networks Tend to Be Insensitive to Signal Settings: Implications for Learning-Based Control

https://github.com/zhouzypaul/tiered-reward

공식 코드

Tiered Reward: Designing Rewards for Specification and Fast Learning of Desired Behavior

https://github.com/zhuliwen/roadnetsz

공식 코드

MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

https://github.com/zilin-huang/HAIM-DRL

공식 코드

HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving

https://github.com/zisikons/deep-rl

공식 코드

Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

https://github.com/ziyuanma/dcc

공식 코드

Learning Selective Communication for Multi-Agent Path Finding

https://github.com/zkshan2002/rto

공식 코드

DPO Meets PPO: Reinforced Token Optimization for RLHF

https://github.com/zminton/TrolleyMod

공식 코드

TrolleyMod v1.0: An Open-Source Simulation and Data-Collection Platform for Ethical Decision Making in Autonomous Vehicles

https://github.com/znowu/mirror-learning

공식 코드

Mirror Learning: A Unifying Framework of Policy Optimisation

https://github.com/zzzace2000/autodiagnosis

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Dynamic Measurement Scheduling for Event Forecasting using Deep RL

https://gitlab.com/AAAL/Fair-RL

공식 코드

Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards

https://gitlab.com/compromise3/desrutge

공식 코드

Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator

https://gitlab.com/hdonancio/pumpscheduling

공식 코드

The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning

https://gitlab.com/marvl-hipt/hipt

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A Hierarchical Approach to Population Training for Human-AI Collaboration

https://gitlab.com/sumo-rl/sumo_openai_gym

공식 코드

Urban Driving with Multi-Objective Deep Reinforcement Learning

https://gitlab.com/xrl2/chat4xai

공식 코드

An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems

https://gitlab.nccr-automation.ch/loris.dinatale/NoDRL

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Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control

https://research-git.uiowa.edu/sanzabizadeh/optimizing_warfarin_dosing_rev_1

공식 코드

Optimizing Warfarin Dosing using Deep Reinforcement Learning

https://zenodo.org/record/4769612

공식 코드

Towards Scalable Verification of Deep Reinforcement Learning

https://github.com/0h-n0/weight_normalization

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/170928/-Review-Dueling-Deep-Q-Network

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/19910101bacon/FinRL_v3

Practical Deep Reinforcement Learning Approach for Stock Trading

https://github.com/1hb6s7t/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/1hb6s7t/Rand-conv

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

https://github.com/1jsingh/rl_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DDAG

Continuous control with deep reinforcement learning

https://github.com/2023-MindSpore-1/ms-code-218/tree/main/deepbsde

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

https://github.com/2023-MindSpore-1/ms-code-52

Playing Atari with Deep Reinforcement Learning

https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_11_optimization

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/2023-MindSpore-4/Code13/tree/main/zhanglan/MLink-main

MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference

https://github.com/2023-MindSpore-4/Code13/tree/main/zhanglan/MLink-main/mlink_ms

MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference

https://github.com/2023-MindSpore-4/Code14/tree/main/RNA

Learning to Design RNA

https://github.com/2023-MindSpore-4/Code6/tree/main/RNA

Learning to Design RNA

https://github.com/2855239858/CenLight-Traffic-Grid-Signal-Optimization-viaAction-and-State-Decomposition

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/4rChon/NL-FuN

Asynchronous Methods for Deep Reinforcement Learning

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