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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/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/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/edwardwardward/crypto_ml

Cryptocurrency Portfolio Management with Deep Reinforcement Learning

https://github.com/efg59/Rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/egipcy/LIRD

Deep Reinforcement Learning for List-wise Recommendations

https://github.com/eladsar/rbi

Distributed Prioritized Experience Replay

https://github.com/elytopia/Info-Sec-Hammerer-and-Brunori

Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

https://github.com/eparisotto/ActorMimic

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

https://github.com/epignatelli/human-level-control-through-deep-reinforcement-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ericborn/binarybot

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/ericchen321/ros_x_habitat

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

https://github.com/erickgalinkin/dropout_privacy

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

https://github.com/ermongroup/CalibratedModelBasedRL

Calibrated Model-Based Deep Reinforcement Learning

https://github.com/ermongroup/EfficientObjectDetection

Efficient Object Detection in Large Images using Deep Reinforcement Learning

https://github.com/ermongroup/higher_order_invariance

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/esmeralday/MARL

Playing Atari with Deep Reinforcement Learning

https://github.com/eth-pbl/elmap-rl-controller

Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

https://github.com/ethanmclark1/carla_aebs

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/eublefar/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/eugenevinitsky/cdc_bottlenecks

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/evangravelle/AI-projects

Thinking Fast and Slow with Deep Learning and Tree Search

https://github.com/evieq01/oodil

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

https://github.com/evzissel/expgen

Decoupling Value and Policy for Generalization in Reinforcement Learning

https://github.com/fabian57fabian/MinGrid-Improved-RL-Methods

Accuracy-based Curriculum Learning in Deep Reinforcement Learning

https://github.com/fabricerosay/AlphaGPU

Monte-Carlo Tree Search as Regularized Policy Optimization

https://github.com/facebookresearch/EgoTV

AI2-THOR: An Interactive 3D Environment for Visual AI

https://github.com/facebookresearch/Horizon

Continuous control with deep reinforcement learning

https://github.com/facebookresearch/ReAgent

Deep Reinforcement Learning with Double Q-learning

https://github.com/facebookresearch/benchmarl

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

https://github.com/facebookresearch/diplomacy_searchbot

Learning to Play No-Press Diplomacy with Best Response Policy Iteration

https://github.com/facebookresearch/habitat-api

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

https://github.com/facebookresearch/habitat-challenge

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

https://github.com/facebookresearch/habitat-sim

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

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