Papers with Code 아카이브

Reinforcement Learning

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보존된 평가 기록

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코드

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

https://github.com/sc2crazy/StarCrackRL

Proximal Policy Optimization Algorithms

https://github.com/schatty/D4PG-pytorch

Continuous control with deep reinforcement learning

https://github.com/schmitzmelissa/unity-ml-penguins

Unity: A General Platform for Intelligent Agents

https://github.com/schroederdewitt/multiagent-particle-envs

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

https://github.com/schrum2/PyribsForGameGAN

Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space

https://github.com/scottemmons/youngs-cql

Soft Actor-Critic Algorithms and Applications

https://github.com/scy6500/blenderbot2-client

ParlAI: A Dialog Research Software Platform

https://github.com/scz233/darts

DARTS: Differentiable Architecture Search

https://github.com/sdicastro/simandrealbettertogether

Hindsight Experience Replay

https://github.com/sea-snell/implicit-language-q-learning

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

https://github.com/seacevedo/ReinforcementLearningProjects

Deep Reinforcement Learning with Double Q-learning

https://github.com/seanpmorgan/tf-weightnorm

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/sebastianrisi/dip

Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures

https://github.com/seblee97/task_weighted_maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/seboz123/mlagents-meta-learning

Unity: A General Platform for Intelligent Agents

https://github.com/secondmind-labs/bayesian_benchmarks

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

https://github.com/self-supervisor/how_to_stay_curious_while_avoiding_noisy_tvs

Large-Scale Study of Curiosity-Driven Learning

https://github.com/semanticweights/tarok

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/semitable/multiagent-particle-envs

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

https://github.com/semitable/robotic-warehouse

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

https://github.com/senya-ashukha/quantile-regression-dqn-pytorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/seolhokim/DistributedRL-Pytorch-Ray

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/seolhokim/SimpleDistributedRL

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/sepsamavi/safe-interactive-crowdnav

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

https://github.com/seriousssam/deep-image-prior-transfer

DARTS: Differentiable Architecture Search

https://github.com/seriousssam/zero-shot-task-generalization-implementation

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning

https://github.com/sermanet/tcn

Time-Contrastive Networks: Self-Supervised Learning from Video

https://github.com/seujung/SNAIL-gluon

A Simple Neural Attentive Meta-Learner

https://github.com/seulkiyeom/once-for-all

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

https://github.com/seungjaeryanlee/combined-experience-replay

A Deeper Look at Experience Replay

https://github.com/seungjaeryanlee/osim-rl-helper

Continuous control with deep reinforcement learning

https://github.com/seungjaeryanlee/rldb

Noisy Networks for Exploration

https://github.com/seungju-k1m/sac-td3-td7

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

https://github.com/sg-nm/Operation-wise-attention-network

Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning

https://github.com/sgillen/fractal_rl

Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement Learning

https://github.com/sgkasselau/pyaixi

A Monte Carlo AIXI Approximation

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