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Reinforcement Learning

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코드

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

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/bonniesjli/PPO-Reacher_UnityML

Proximal Policy Optimization Algorithms

https://github.com/bonniesjli/PPO_Reacher

Proximal Policy Optimization Algorithms

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/botforge/simplementation

Deep Reinforcement Learning with Double Q-learning

https://github.com/bowenbaker/metaqnn

Designing Neural Network Architectures using Reinforcement Learning

https://github.com/bprabhakar/upside-down-reinforcement-learning

Training Agents using Upside-Down Reinforcement 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/brandontrabucco/efficient-hrl

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/brian-moser/dartsrenet

DARTS: Differentiable Architecture Search

https://github.com/bruzwen/ddxplus

Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification

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/bshall/VectorQuantizedCPC

Representation Learning with Contrastive Predictive Coding

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

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/bukysun/gail-traj-eb

Generative Adversarial Imitation Learning

https://github.com/caggursoy/Cross-Age-Effect

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

https://github.com/caggursoy/crossageeffect

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

https://github.com/cair/PyTsetlinMachineCUDA

Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/cair/pyTsetlinMachine

Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/cair/pyTsetlinMachineMT

Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/cair/pyTsetlinMachineParallel

Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/caiyingchun/MolDQN

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/calclavia/NOS

Neural Optimizer Search with Reinforcement Learning

https://github.com/camall3n/atari-MAC

Mean Actor Critic

https://github.com/cannylab/casual_overhypotheses

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/caravagn/GDA

Gradient Estimation Using Stochastic Computation Graphs

https://github.com/carlospurves/psxle

The PlayStation Reinforcement Learning Environment (PSXLE)

https://github.com/carlotes247/IGGI19_Imitation_Learning_Workshop

Unity: A General Platform for Intelligent Agents

https://github.com/carpedm20/ENAS-pytorch

Neural Architecture Search with Reinforcement Learning

https://github.com/carperai/trlx

Fine-Tuning Language Models from Human Preferences

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