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Reinforcement Learning

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

https://github.com/arryyao/flow-project-flow

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/ars-ashuha/quantile-regression-dqn-pytorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/arunajit/drl

Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning

https://github.com/arunavkonwar/world-models

World Models

https://github.com/asannasi/txt_adv_nlp

TextWorld: A Learning Environment for Text-based Games

https://github.com/asarav/MIT-Deep-Traffic-Solution

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/asgerhb/state-space-transformation-shielding

Shielded Reinforcement Learning for Hybrid Systems

https://github.com/ash3n/Meta-Gradients

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

https://github.com/ash3n/Meta-SGD

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

https://github.com/ash3n/Meta-SGD-TF

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

https://github.com/ashaaher/Reinforcement-Learning-Project

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/asharakeh/probdet

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

https://github.com/ashishpatel26/GRADCAM-Tensorflow2-Visual-Explainable-AI

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

https://github.com/ashtawy/deeptraffic

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/ashwin-M-D/DM-Gym

OR-Gym: A Reinforcement Learning Library for Operations Research Problems

https://github.com/asjad99/Reinforcement-Learning

Deep Q-learning from Demonstrations

https://github.com/asonabend/dtr_via_surrogate_loss

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation

https://github.com/aspk/Tensorflow_Deep_RL

Parameter Space Noise for Exploration

https://github.com/aspk/space_battle

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/astanway/gated-conv-nets

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

https://github.com/astooke/accel_rl

Accelerated Methods for Deep Reinforcement Learning

https://github.com/astooke/rlpyt/tree/master/rlpyt/ul

Decoupling Representation Learning from Reinforcement Learning

https://github.com/astrid-merckling/SRL4RL

Soft Actor-Critic Algorithms and Applications

https://github.com/atavakol/action-hypergraph-networks

Prioritized Experience Replay

https://github.com/atgambardella/pytorch-es

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/atpcurr/atpcurr

Towards Finding Longer Proofs

https://github.com/attentionagent/attentionagent.github.io

Neuroevolution of Self-Interpretable Agents

https://github.com/aurelio-amerio/concretedropout

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

https://github.com/aus10powell/Automated-Health-Responses

Learning through Dialogue Interactions by Asking Questions

https://github.com/austinsilveria/Banana-Collection-DQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/automl-research/nscaching

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/autonomousvision/CaRL

End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

https://github.com/autumnwu/aggressive-q-learning-with-ensembles

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

https://github.com/avalon-benchmark/avalon

Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds

https://github.com/aviatesk/street-feature-analysis

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

https://github.com/avillemin/Minecraft-AI

Playing Atari with Deep Reinforcement Learning

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