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/gd-goblin/C-3PO_Motion_Retargeting_Module

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

https://github.com/gd-goblin/C-3PO_RobotControlModule

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

https://github.com/geeky-wizard/Atari-Deep-Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/geohot/twitchcoq

Value Prediction Network

https://github.com/georgeliu233/scene-rep-transformer

Model-free Deep Reinforcement Learning for Urban Autonomous Driving

https://github.com/georgkruse/cleanqrl

Continuous control with deep reinforcement learning

https://github.com/ghliu/pytorch-ddpg

Continuous control with deep reinforcement learning

https://github.com/ghostFaceKillah/expert

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/gijskoning/ReproducingCURL

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/gingkg/multiagent-particle-envs

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/github-jnauta/pytorch-pne

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

https://github.com/gjp1203/nui_in_madrl

Lenient Multi-Agent Deep Reinforcement Learning

https://github.com/glassices/sokoban_planner

Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning

https://github.com/goktug97/bohb-hpo

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

https://github.com/goldbattle/snakes_mal

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/gomerudo/nas-dmrl

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/gomerudo/openai-baselines

Learning to reinforcement learn

https://github.com/google-deepmind/acme

Acme: A Research Framework for Distributed Reinforcement Learning

https://github.com/google-deepmind/pysc2

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/google-deepmind/scalable_agent

Learning to Navigate in Cities Without a Map

https://github.com/google-deepmind/spriteworld

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration

https://github.com/google-deepmind/streetlearn

Learning To Follow Directions in Street View

https://github.com/google-research/batch_rl

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

https://github.com/google-research/google-research/tree/master/bisimulation_aaai2020

Dopamine: A Research Framework for Deep Reinforcement Learning

https://github.com/google/maddpg-replication

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/gordicaleksa/pytorch-learn-reinforcement-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/gouxiangchen/dueling-DQN-pytorch

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/gowriaddepalli/ML_DL_Research_collab_base

Deep Reinforcement Learning framework for Autonomous Driving

https://github.com/grananqvist/reinforcement-learning-super-mario-A3C

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/grass123-hub/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/grtzsohalf/SpeechNet-codebase

Gradient Surgery for Multi-Task Learning

https://github.com/gtegner/hyper-gan

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/guillaumeboniface/bananaland

Deep Reinforcement Learning with Double Q-learning

https://github.com/guillaumeboniface/reacher

Continuous control with deep reinforcement learning

https://github.com/gungui98/deeprl-a3c-ai2thor

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/guojm14/HVF

Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment

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