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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/dpoulopoulos/drl_collaborate_compete

Continuous control with deep reinforcement learning

https://github.com/dpoulopoulos/drl_continuous_control

Continuous control with deep reinforcement learning

https://github.com/dragen1860/DARTS-PyTorch

DARTS: Differentiable Architecture Search

https://github.com/dragen1860/MAML-Pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/dragen1860/MAML-Pytorch-RL

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

https://github.com/dragen1860/Reptile-Pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/drblallo/open_spiel

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/drduda/ReAllY

One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

https://github.com/drforester/Q-learning-Intersection-Crossing

Playing Atari with Deep Reinforcement Learning

https://github.com/drozzy/upsidedown

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

https://github.com/dsapandora/s_cera

Policy Distillation

https://github.com/dsbrown1331/safe-imitation-learning

Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning

https://github.com/dsgiitr/rl_2048

Playing Atari with Deep Reinforcement Learning

https://github.com/dshchung/local_minmax_gans

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

https://github.com/dsinghnegi/atari_RL_agent

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/dtabas/epymarl

The StarCraft Multi-Agent Challenge

https://github.com/dtak/hip-mdp-public

Prioritized Experience Replay

https://github.com/dtak/policy-summarization-with-user-models-public

Exploring Computational User Models for Agent Policy Summarization

https://github.com/dtanoglidis/DeepShadows

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/dti-research/SenseAct

Setting up a Reinforcement Learning Task with a Real-World Robot

https://github.com/durashi/Dialog_policy_network_for_low_resource_setting

A User Simulator for Task-Completion Dialogues

https://github.com/dvij542/offtersim

Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning

https://github.com/dwjshift/IL_ADS

Imitation Learning from Observation with Automatic Discount Scheduling

https://github.com/dxyang/DQN_pytorch

Deep Reinforcement Learning with Double Q-learning

https://github.com/dyabel/handson_rl

Proximal Policy Optimization Algorithms

https://github.com/dylancashman/remap_nas

Designing Neural Network Architectures using Reinforcement Learning

https://github.com/dyth/doublegum

Continuous control with deep reinforcement learning

https://github.com/e155763/GAN-RL

Surprising Negative Results for Generative Adversarial Tree Search

https://github.com/eac-replication/eac-replication

Accelerated Methods for Deep Reinforcement Learning

https://github.com/eambutu/snail-pytorch

A Simple Neural Attentive Meta-Learner

https://github.com/ebadrian/metadl

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/eclique/keras-gradcam

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/eddynelson/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/edlanglois/relearn

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

https://github.com/edmundmills/basalt-competition

SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

https://github.com/eduardosebastianrodriguez/phmarl

Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

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