Papers with Code 아카이브

Deep Reinforcement Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

코드

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

https://github.com/prosysscience/JSS

공식 코드

A Reinforcement Learning Environment For Job-Shop Scheduling

https://github.com/prosysscience/JSSEnv

공식 코드

A Reinforcement Learning Environment For Job-Shop Scheduling

https://github.com/psclklnk/spdl

공식 코드

Self-Paced Deep Reinforcement Learning

https://github.com/psjanani/RLAProject

공식 코드

Resolving Implicit Coordination in Multi-Agent Deep Reinforcement Learning with Deep Q-Networks & Game Theory

https://github.com/purdue-biorobotics/flappy

공식 코드

Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals

https://github.com/puyuan1996/LC-SAC

공식 코드

Context-Based Soft Actor Critic for Environments with Non-stationary Dynamics

https://github.com/puyuan1996/MARL

공식 코드

Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning

https://github.com/pytorch/rl

공식 코드

RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

https://github.com/qcappart/ROD_oracle

공식 코드

How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman Problem

https://github.com/qcappart/hybrid-cp-rl-solver

공식 코드

Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

https://github.com/qcappart/learning-DD

공식 코드

Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

https://github.com/qiliuchn/data-corruption-study

공식 코드

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies

https://github.com/qiongwu86/Cooperative-Caching-in-VEC-Based-on-Asynchronous-FL-and-DRL

공식 코드

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

https://github.com/qiongwu86/DDPG-RIS-MADDPG-POWER

공식 코드

Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation

https://github.com/qiongwu86/drl-based-optimization-for-information-of-age-and-energy-consumption-in-c-v2x-enabled-iov

공식 코드

DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

https://github.com/qiongwu86/drl-bfssl

공식 코드

DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV

https://github.com/qiongwu86/edge-caching-based-on-multi-agent-deep-reinforcement-learning-and-federated-learning

공식 코드

Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network

https://github.com/qiongwu86/gnn-and-drl-based-resource-allocation-for-v2x-communications

공식 코드

Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications

https://github.com/qiongwu86/joint-optimization-of-aoi-and-energy-consumption-in-nr-v2x-system-based-on-drl

공식 코드

Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning

https://github.com/qiongwu86/optimizing-aoi-in-vec-with-federated-graph-neural-network-multi-agent-reinforcement-learning

공식 코드

Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning

https://github.com/qiongwu86/resource-allocation-for-twin-maintenance-and-computing-tasks-in-digital-twin-mobile-edge-network

공식 코드

Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network

https://github.com/qiongwu86/ris-rb-aoi-v2x-drl

공식 코드

Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks

https://github.com/qiongwu86/ris-vec-marl

공식 코드

Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning

https://github.com/qiongwu86/semantic-aware-spectrum-sharing-in-internet-of-vehicles-based-on-deep-reinforcement-learning

공식 코드

Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning

https://github.com/qiujumin/don-rl

공식 코드

Decision-making and control with diffractive optical networks

https://github.com/qlan3/MeDQN

공식 코드

Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation

https://github.com/qlt315/ucmec-mmwave-fronthaul

공식 코드

Towards Decentralized Task Offloading and Resource Allocation in User-Centric Mobile Edge Computing

https://github.com/qq456cvb/doudizhu-C

공식 코드

Combinational Q-Learning for Dou Di Zhu

https://github.com/quantumiracle/Cascading-Decision-Tree

공식 코드

CDT: Cascading Decision Trees for Explainable Reinforcement Learning

https://github.com/quantumiracle/mars

공식 코드

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

https://github.com/quantumiracle/nash-dqn

공식 코드

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

https://github.com/quanvuong/Supervised_Policy_Update

공식 코드

Supervised Policy Update for Deep Reinforcement Learning

https://github.com/qutrobotlearning/batchlearning

공식 코드

Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks

https://github.com/qyyin/Reinforce4ZP

공식 코드

Deep Reinforcement Learning for Chinese Zero pronoun Resolution

https://github.com/rajammanabrolu/KG-DQN

공식 코드

Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning

https://github.com/ral94/rlcbm

공식 코드

Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line Systems

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