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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/TeaPearce/Anchored_Ens_RL_Explore

Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning

https://github.com/TeaPearce/Expressive_Priors_in_BNNs

Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions

https://github.com/TeaPearce/RL_Cart_Pole_Speed_Run

Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning

https://github.com/TeaganLi/ICRA2020_manipulation

Learning Hierarchical Control for Robust In-Hand Manipulation

https://github.com/Techget/gail-tf-sc2

Generative Adversarial Imitation Learning

https://github.com/Teslatic/SC2-Freiburg

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/TheAbhiKumar/tensorflow-value-iteration-networks

Value Iteration Networks

https://github.com/TheFebrin/DeepRL-Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/TheInfamousWayne/CheXNet

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

https://github.com/TheInfamousWayne/ddpg

Continuous control with deep reinforcement learning

https://github.com/ThirstyScholar/bayes-by-backprop

Weight Uncertainty in Neural Networks

https://github.com/ThomasGoerttler/similarity-analysis-of-maml

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

https://github.com/TianhongDai/hindsight-experience-replay

Hindsight Experience Replay

https://github.com/Tikquuss/meta_XLM

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

https://github.com/TinkeringCode/Neural-Fictitous-Self-Play

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

https://github.com/TobeyYang/S2S_Temp

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/TobiKick/DL_LF2

Deep Reinforcement Learning for Playing 2.5D Fighting Games

https://github.com/TonghanWang/DOP

Multiagent Cooperation and Competition with Deep Reinforcement Learning

https://github.com/TooTouch/WhiteBox-Part1

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

https://github.com/ToolManChang/DRLseminar_code_challenge

Soft Actor-Critic for Discrete Action Settings

https://github.com/TreeLimes/QANAS

Neural Architecture Search with Reinforcement Learning

https://github.com/TrentBrick/RewardConditionedUDRL

Training Agents using Upside-Down Reinforcement Learning

https://github.com/TristanBertin/Reinforcement_Learning

Modern Deep Reinforcement Learning Algorithms

https://github.com/Tyjch/open_spiel

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/Tyjch/open_spiel_colab

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/UKPLab/emnlp2019-summary-reward

Better Rewards Yield Better Summaries: Learning to Summarise Without References

https://github.com/UWaterloo-ASL/option-critic-architecture

The Option-Critic Architecture

https://github.com/Uchman21/RAW

Recurrent Attention Walk for Semi-supervised Classification

https://github.com/UesugiErii/tf2-PPO-atari

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/UlisseMini/procgen-tools

Goal Misgeneralization in Deep Reinforcement Learning

https://github.com/UnibucProjects/DeepRLRecommenderSystem

Deep Reinforcement Learning for List-wise Recommendations

https://github.com/UrosOgrizovic/SimpleGoogleQuickdraw

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/V0LsTeR/DQN_heap

Prioritized Experience Replay

https://github.com/V0LsTeR/dopamine_prioritized_buffer

Prioritized Experience Replay

https://github.com/VD44/Rouge-L-Tensorflow

A Deep Reinforced Model for Abstractive Summarization

https://github.com/VasaKiDD/TD3-deep-rl-research

Continuous control with deep reinforcement learning

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