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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/sourenaKhanzadeh/snakeAi

Playing Atari with Deep Reinforcement Learning

https://github.com/sparkmxy/my-offlinerl

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/spragunr/deep_q_rl

Playing Atari with Deep Reinforcement Learning

https://github.com/sradicwebster/mbrl-lib

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

https://github.com/ssainz/reinforcement_learning_algorithms

Deep Reinforcement Learning with Double Q-learning

https://github.com/ssokota/mmd

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

https://github.com/starry-sky6688/MADDPG

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

https://github.com/stat-ml/dpp-dropout-uncertainty

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

https://github.com/statsu1990/regression_dropout

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

https://github.com/stephen-chung-mh/map_prop

MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents

https://github.com/stevenpjg/ddpg-aigym

Continuous control with deep reinforcement learning

https://github.com/stevep2007/https-github.com-ZhengyaoJiang-PGPortfolio

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/stevezhang1990/dma_rl

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

https://github.com/stewartyoung/DeepRL-PM

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/subhadip-maiti/tinydqn

Playing Atari with Deep Reinforcement Learning

https://github.com/sunfex/weighted-sac

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

https://github.com/sunjeet95/Deep-Q-Network-using-Tensorflow

Playing Atari with Deep Reinforcement Learning

https://github.com/super5-22/habitat-vis

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

https://github.com/susan-amin/SparseBaseline1

Continuous control with deep reinforcement learning

https://github.com/susumuota/gym-oculoenv

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents

https://github.com/swordest/mec_drl

Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach

https://github.com/sygi/deep_q_rl

Playing Atari with Deep Reinforcement Learning

https://github.com/taku-y/20181125-pybullet

Continuous control with deep reinforcement learning

https://github.com/takuseno/unreal

Reinforcement Learning with Unsupervised Auxiliary Tasks

https://github.com/tangxiangru/RL-for-RNA-design

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/taodav/TextWorldACG

Towards Solving Text-based Games by Producing Adaptive Action Spaces

https://github.com/tarod13/SAC

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

https://github.com/tccnchsu/study

A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation

https://github.com/tcmxx/CNTKUnityTools

Playing Atari with Deep Reinforcement Learning

https://github.com/tegg89/DLCamp_Jeju2018

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnn

Continuous control with deep reinforcement learning

https://github.com/tegg89/mann

Continuous control with deep reinforcement learning

https://github.com/tennisonliu/bayesian-neural-network

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

https://github.com/tensorflow/deepmath/tree/master/deepmath/deephol

HOList: An Environment for Machine Learning of Higher-Order Theorem Proving

https://github.com/tensorflow/models

Parameter Space Noise for Exploration

https://github.com/tensorflow/models/tree/archive/research/deep_contextual_bandits

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

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