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Deep Reinforcement Learning

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

https://github.com/junyoung0131/Mol-DQN

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/justinjfu/diagnosing_qlearning

Diagnosing Bottlenecks in Deep Q-learning Algorithms

https://github.com/jviquerat/pbo

Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows

https://github.com/jvking/text-games

Deep Reinforcement Learning with a Natural Language Action Space

https://github.com/jvoynow/DQN-analysis-with-2048

Deep Reinforcement Learning with Double Q-learning

https://github.com/jyqhahah/rl_maddpg_matd3

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

https://github.com/k-r-allen/residual-policy-learning

Residual Policy Learning

https://github.com/kachayev/gym-microrts-paper-sb3

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/kairproject/kair_algorithms_draft

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

https://github.com/kaletap/deep-cube-rl

Solving the Rubik's Cube Without Human Knowledge

https://github.com/kargarisaac/macrpo

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

https://github.com/karishnu/tf-agents-multi-particle-envs

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

https://github.com/katalinic/l2rl

Learning to reinforcement learn

https://github.com/kathyrnrouse/RL_CUIP

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

https://github.com/kdexd/lang-emerge-parlai

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

https://github.com/keep9oing/DRQN-Pytorch-CartPole-v1

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/kenfuliang/Awesome-Reinforcement-Learning

DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills

https://github.com/kenya-sk/mc_dropout_tensorflow

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

https://github.com/kered9/gym-microrts

Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS

https://github.com/kevin5naug/summer_project

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/kevslinger/dtqn

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/kftam1994/Robo_Advisor

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/khanhptnk/bandit-nmt

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/kimbring2/DQFD_Minecraft

Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft

https://github.com/kiminh/dual-policy-distillation

Dual Policy Distillation

https://github.com/kinalmehta/epymarl

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

https://github.com/kishorkuttan/Multi-Agent-Deep-Deterministic-Policy-Gradient-actor-critic-deep-reinforcement-for-capacity-manageme

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

https://github.com/kklipski/ALHE-projekt

Population Based Training of Neural Networks

https://github.com/kkonen/ros2learn

ROS2Learn: a reinforcement learning framework for ROS 2

https://github.com/kmdanielduan/DQN_Family_PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/kngwyu/intrinsic-rewards

Exploration by Random Network Distillation

https://github.com/kochlisGit/autonomous-vehicles-agent

Deep Reinforcement Learning with Double Q-learning

https://github.com/komejisatori/ReinforcementCar

Playing Atari with Deep Reinforcement Learning

https://github.com/kondrasso/DQN

Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

https://github.com/kosyoshida/simple-keras

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

https://github.com/kousun12/TFDE

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

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