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/rpatrik96/AttA2C

공식 코드

Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning

https://github.com/rraileanu/auto-drac

공식 코드

Automatic Data Augmentation for Generalization in Deep Reinforcement Learning

https://github.com/rraileanu/idaac

공식 코드

Decoupling Value and Policy for Generalization in Reinforcement Learning

https://github.com/rtealwitter/openstreets

공식 코드

I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives

https://github.com/rtloftin/hi-c

공식 코드

Uncoupled Learning of Differential Stackelberg Equilibria with Commitments

https://github.com/rubai5/ESS_Game

공식 코드

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?

https://github.com/rubenrtorrado/GVGAI_GYM

공식 코드

Deep Reinforcement Learning for General Video Game AI

https://github.com/ruihuang1967/Deep-GRAIL

공식 코드

Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR Streaming

https://github.com/ruizhaogit/GuessWhat-TemperedPolicyGradient

공식 코드

Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient

https://github.com/rusfortunat/coalescence_w_deeprl

공식 코드

Controlling dynamics of stochastic systems with deep reinforcement learning

https://github.com/rwickman/sparrl-pytorch

공식 코드

A Generic Graph Sparsification Framework using Deep Reinforcement Learning

https://github.com/sa-and/core

공식 코드

CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning

https://github.com/saeedmarzban/wavecorr

공식 코드

WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio Management

https://github.com/saferoboticslab/safety_rl

공식 코드

Safety and Liveness Guarantees through Reach-Avoid Reinforcement Learning

https://github.com/sagar-pa/crystalbox

공식 코드

CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems

https://github.com/sahars93/rl-navigation

공식 코드

Sim-to-Real Transfer for Mobile Robots with Reinforcement Learning: from NVIDIA Isaac Sim to Gazebo and Real ROS 2 Robots

https://github.com/saisrivatsan/deep-seq-auctions

공식 코드

Deep Reinforcement Learning for Sequential Combinatorial Auctions

https://github.com/salesforce/ai-economist

공식 코드

The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning

https://github.com/salesforce/coderl

공식 코드

CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

https://github.com/salesforce/rirl

공식 코드

Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal

https://github.com/salesforce/warp-drive

공식 코드

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

https://github.com/salwamostafa/intent-aware-drl-based-noma-uplink-dynamic-scheduler-for-iiot

공식 코드

Intent-Aware DRL-Based NOMA Uplink Dynamic Scheduler for IIoT

https://github.com/samuelschmidgall/surgicalgym

공식 코드

Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots

https://github.com/sangwkim/RL_manufacturing

공식 코드

Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement Learning

https://github.com/sanketkshah/NeurADP-for-Ride-Pooling

공식 코드

Neural Approximate Dynamic Programming for On-Demand Ride-Pooling

https://github.com/scale-lab/DRiLLS

공식 코드

DRiLLS: Deep Reinforcement Learning for Logic Synthesis

https://github.com/scil-vital/tracktolearn

공식 코드

What Matters in Reinforcement Learning for Tractography

https://github.com/scslabisu/aggym

공식 코드

AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning

https://github.com/sdpkjc/snapshotrl

공식 코드

Snapshot Reinforcement Learning: Leveraging Prior Trajectories for Efficiency

https://github.com/seanjia/gsl

공식 코드

Improving Policy Optimization with Generalist-Specialist Learning

https://github.com/sebastian-griesbach/see

공식 코드

Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization

https://github.com/sebastianrisi/ga-world-models

공식 코드

Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/sensl/andes_gym

공식 코드

Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems

https://github.com/setarehc/deep_rl_regions

공식 코드

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning

https://github.com/seu-proactivesecurity-group/pg-fmadrl

공식 코드

From Static to Adaptive Defense: Federated Multi-Agent Deep Reinforcement Learning-Driven Moving Target Defense Against DoS Attacks in UAV Swarm Networks

https://github.com/sfujim/BCQ

공식 코드

Off-Policy Deep Reinforcement Learning without Exploration

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