Papers with Code 아카이브

Deep Reinforcement Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

코드

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

https://github.com/bjotho/Zelda1AI

Playing Atari with Deep Reinforcement Learning

https://github.com/bkhmsi/meta-rl-harlow

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/blacKitten13/Dropout-for-Model-Uncertainty

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

https://github.com/blakeMilner/DeepQLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/bmeyers/VirtualMicrogridSegmentation

Continuous control with deep reinforcement learning

https://github.com/bonniesjli/MADDPG_Tennis

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

https://github.com/bonniesjli/MADDPG_Tennis_UnityML

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

https://github.com/borea17/efficient_rl

Playing Atari with Deep Reinforcement Learning

https://github.com/borhanreo/Obstacle-Avoid-Car

Playing Atari with Deep Reinforcement Learning

https://github.com/bossdm/LifelongRL

Selective Experience Replay for Lifelong Learning

https://github.com/botforge/simplementation

Deep Reinforcement Learning with Double Q-learning

https://github.com/brain-research/LeaveNoTrace

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

https://github.com/brain-research/mirage-rl

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

https://github.com/braincog-x/brain-cog

Spiking World Model with Multi-Compartment Neurons for Model-based Reinforcement Learning

https://github.com/brando130/CSharp_NN

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

https://github.com/bryonkucharski/Learning-to-Drive-with-Reinforcement-Learning-and-Variational-Autoencoders

Learning to Drive in a Day

https://github.com/bryonkucharski/learning-to-drive-in-a-day-reproduction

Learning to Drive in a Day

https://github.com/bstadie/third_person_im

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/bucky1995/Portfolio-RL

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/caiyingchun/MolDQN

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/caslab-vt/SARNet

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

https://github.com/catalyst-team/catalyst-rl-framework

Catalyst.RL: A Distributed Framework for Reproducible RL Research

https://github.com/cathywu/rllab-multiagent

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/cbfinn/maml_rl

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/cbschaff/nlimb

Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning

https://github.com/ccolas/rl_stats

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

https://github.com/cdebeunne/uncertainties_CNN

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

https://github.com/cdesilv1/sc2_ai_cdes

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/chandar-lab/RLHive

Playing Atari with Deep Reinforcement Learning

https://github.com/changpowei/MicroRTS

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

https://github.com/chenhongge/SA_DQN

Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

https://github.com/cindycia/Atari-SAC-Discrete

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

https://github.com/cjy1992/gym-carla

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

https://github.com/ckjzsa/wwtp_opt_rl

Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning

https://github.com/clarky104/carla_aebs

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/claudeHifly/BipedalWalker-v3

Continuous control with deep reinforcement learning

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