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Reinforcement Learning

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코드

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

https://github.com/gwthomas/iql-pytorch

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/gyutaaa/gyutaekim-2016011728

Unity: A General Platform for Intelligent Agents

https://github.com/gznyyb/deep_reinforcement_learning_Pong

Deep Reinforcement Learning with Double Q-learning

https://github.com/gzrjzcx/ML-agents

Unity: A General Platform for Intelligent Agents

https://github.com/h-aboutalebi/SparceReward

Continuous control with deep reinforcement learning

https://github.com/hagianga21/MaxCon_RL

Unsupervised Learning for Robust Fitting:A Reinforcement Learning Approach

https://github.com/hahayonghuming/VDACs

The StarCraft Multi-Agent Challenge

https://github.com/haiguanl/DQN_GlobalRouting

A Deep Reinforcement Learning Approach for Global Routing

https://github.com/haje01/distper

Distributed Prioritized Experience Replay

https://github.com/haje01/impala

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/hajohajo/TrainingFrameworkTrackDNN

What is the Effect of Importance Weighting in Deep Learning?

https://github.com/hal-314/fastai-batch-size-finder

An Empirical Model of Large-Batch Training

https://github.com/halajadallah/RL-Quadcopter_project

Continuous control with deep reinforcement learning

https://github.com/hamidreza-mazandarani/SAMA-D3QL

A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications

https://github.com/hamishs/JAX-RL

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/hammer-wang/oml-ppo/blob/master/README.md

Automated Optical Multi-layer Design via Deep Reinforcement Learning

https://github.com/han-won/PlayingAtariWithMindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/hanhanAnderson/LSF-SAC

Counterfactual Multi-Agent Policy Gradients

https://github.com/hansungj/BayesianNeuralNetworkDiscretizedGibbsSampler

Weight Uncertainty in Neural Networks

https://github.com/hanyas/reps

f-Divergence constrained policy improvement

https://github.com/haosulab/ManiSkill-Learn

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/hardmaru/WorldModelsExperiments

World Models

https://github.com/hari-sikchi/loop

Learning Off-Policy with Online Planning

https://github.com/hari-sikchi/pytorch_CPO

Constrained Policy Optimization

https://github.com/haron1100/Upside-Down-Reinforcement-Learning

Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions

https://github.com/harruff/Senior_Project_Repository

Proximal Policy Optimization Algorithms

https://github.com/harrybraviner/self_directed_rl

WALL-E: An Efficient Reinforcement Learning Research Framework

https://github.com/harshit0511/Inv-RL

Market Self-Learning of Signals, Impact and Optimal Trading: Invisible Hand Inference with Free Energy

https://github.com/harvard-edge/source-seeking

Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller

https://github.com/harvitronix/reinforcement-learning-car

Playing Atari with Deep Reinforcement Learning

https://github.com/hbahadirsahin/bayes_by_backprop

Weight Uncertainty in Neural Networks

https://github.com/hbzhang/AwesomeSelfDriving

Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving

https://github.com/hcch0912/gym_wrapper_dm_soccer

Arena: a toolkit for Multi-Agent Reinforcement Learning

https://github.com/hdilab/world_model_experiments

World Models

https://github.com/hdparks/AsteroidsDeepReinforcement

Proximal Policy Optimization Algorithms

https://github.com/hebowei2000/deep-reinforcement-learning

Data-Efficient Hierarchical Reinforcement Learning

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