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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/marwanihab/RL_Testing_Noise_ASRN

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

https://github.com/massquantity/DBRL

Continuous control with deep reinforcement learning

https://github.com/mathformoso/RL-playground---ddpg

Continuous control with deep reinforcement learning

https://github.com/matthewsparr/Deep-Zork

Language Understanding for Text-based Games Using Deep Reinforcement Learning

https://github.com/matthewsparr/Reinforcement-Learning-Lesson

Continuous control with deep reinforcement learning

https://github.com/matus-jan-lavko/ReinforcementLearning-vs-EW

FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

https://github.com/mauricemager/multiagent-robot

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

https://github.com/mavischer/DRRL

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/maxiboether/mis-benchmark-framework

Learning What to Defer for Maximum Independent Sets

https://github.com/maziarg/PrivAttack-BCQ

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/megvii-research/ICCV2019-LearningToPaint

Learning to Paint With Model-based Deep Reinforcement Learning

https://github.com/megvii-research/NIPS2017-LearningToRunACE

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

https://github.com/mehdimashayekhi/Some-RL-Implementation

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/metataro/minerl_agent

Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft

https://github.com/mf1832146/tree-transformer

Improving Automatic Source Code Summarization via Deep Reinforcement Learning

https://github.com/mf1832146/tree_transformer_2.0

Improving Automatic Source Code Summarization via Deep Reinforcement Learning

https://github.com/mfregeau/DeepLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/miaoruonan/MACA_test

Socially Aware Motion Planning with Deep Reinforcement Learning

https://github.com/michael-snower/ppo

Accelerated Methods for Deep Reinforcement Learning

https://github.com/michaelnny/deep_rl_zoo

Playing Atari with Deep Reinforcement Learning

https://github.com/michaelwan11/MIKT

Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch

https://github.com/michalnand/reinforcement_learning

Exploration by Random Network Distillation

https://github.com/microsoft/logrl

Dopamine: A Research Framework for Deep Reinforcement Learning

https://github.com/microsoft/med-deadend

Deep Reinforcement Learning with Double Q-learning

https://github.com/microsoft/orchestrated-value-mapping

Dopamine: A Research Framework for Deep Reinforcement Learning

https://github.com/microsoft/strategically_efficient_rl

Exploration by Random Network Distillation

https://github.com/mightypirate1/DRL-Tetris

Deep Reinforcement Learning with Double Q-learning

https://github.com/mila-iqia/climate-cooperation-competition

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

https://github.com/milutter/deep_lagrangian_networks

Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

https://github.com/milutter/value_iteration

Value Iteration in Continuous Actions, States and Time

https://github.com/mindspore-ai/models/tree/master/research/hpc/deepbsde

Solving high-dimensional partial differential equations using deep learning

https://github.com/mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/mindspore-courses/Rainbow-MindSpore

Deep Reinforcement Learning with Double Q-learning

https://github.com/minerllabs/minerl

MineRL: A Large-Scale Dataset of Minecraft Demonstrations

https://github.com/mingyip/Rainbow-Reinforcement-Learning-For-Chess-Games

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/minosworld/minos

MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments

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