Papers with Code 아카이브

Reinforcement Learning

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코드

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

https://github.com/unreliableXu/TensorFlow_RLRE

Reinforcement Learning for Relation Classification from Noisy Data

https://github.com/unstablecursor/robotino_rl

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

https://github.com/uoe-agents/ksl

Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning

https://github.com/uoe-agents/revisiting-maddpg

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

https://github.com/uos-plccn/gym-pbn

Deep Reinforcement Learning for Control of Probabilistic Boolean Networks

https://github.com/upb-lea/gem-control

Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control

https://github.com/uribyul/py_ai_clinician

Deep Reinforcement Learning for Sepsis Treatment

https://github.com/urw7rs/spiralpp

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

https://github.com/ustc-arg/safe-robot-learning-competition

Safe Exploration in Continuous Action Spaces

https://github.com/utarumo/RL_implementation

Deep Reinforcement Learning with Double Q-learning

https://github.com/uvipen/Super-mario-bros-A3C-pytorch

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/uvipen/super-mario-bros-ppo-pytorch

Proximal Policy Optimization Algorithms

https://github.com/vadim0x60/cibi

Neurogenetic Programming Framework for Explainable Reinforcement Learning

https://github.com/vadimkantorov/ctc

A Stable and Effective Learning Strategy for Trainable Greedy Decoding

https://github.com/valko073/LyricsGANs

Long Text Generation via Adversarial Training with Leaked Information

https://github.com/varuncs2011/rl

OpenAI Gym

https://github.com/varunjain3/SafetyRL_HighwayEnv

Constrained Policy Optimization

https://github.com/vbbphc/GradCAM.jl

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

https://github.com/vcadillog/PPO-Mario-Bros-Tensorflow-2

Proximal Policy Optimization Algorithms

https://github.com/vcharvet/project-rl

Deep reinforcement learning from human preferences

https://github.com/vdean/audio-curiosity

Large-Scale Study of Curiosity-Driven Learning

https://github.com/vectorinstitute/neuralkernelbandits

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

https://github.com/vedantc6/SeqGAN

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/veds12/torch_rl_squared

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/veegee82/DARTS

DARTS: Differentiable Architecture Search

https://github.com/vendyv/CodeSpeedy_Internship

Representation Learning with Contrastive Predictive Coding

https://github.com/venkateshdudipalli/Self-Driving-Car

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/vergilus/NJUNMT-pytorch/tree/master/adversarials

A Reinforced Generation of Adversarial Examples for Neural Machine Translation

https://github.com/vermashresth/damage-aware-PPO

Proximal Policy Optimization Algorithms

https://github.com/vermashresth/damage-sim-tf-agents

TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

https://github.com/vermashresth/sequential_social_dilemma_games

Multi-agent Reinforcement Learning in Sequential Social Dilemmas

https://github.com/vgudapati/DRLND_Collaboration_Competetion

Emergent Complexity via Multi-Agent Competition

https://github.com/vheuthe/microbot_rl

Proximal Policy Optimization Algorithms

https://github.com/victcebesp/Booster-Evolved-Policy-Gradient

Evolved Policy Gradients

https://github.com/victcebesp/EPGVariation

Evolved Policy Gradients

https://github.com/victorchan314/cs287_final_project

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

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