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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/leggedrobotics/legged_gym

Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

https://github.com/leggedrobotics/rsl_rl

Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

https://github.com/leiapollos/ml-agents-iaj-selfdrivingcar

Unity: A General Platform for Intelligent Agents

https://github.com/lemon234071/oc_parlai

ParlAI: A Dialog Research Software Platform

https://github.com/leo27945875/ASAF-1

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

https://github.com/leo27945875/Adversarial-Soft-Advantage-Fitting-1

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

https://github.com/leoandeol/cods

PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

https://github.com/leopard-ai/betty

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

https://github.com/lerrel/gym-adv

Robust Adversarial Reinforcement Learning

https://github.com/lerrel/rllab-adv

Robust Adversarial Reinforcement Learning

https://github.com/letian-wang/asaprl

Accelerating Reinforcement Learning with Learned Skill Priors

https://github.com/lexfridman/deeptraffic

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/lflfdxfn/DPLAN-Implementation

Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data

https://github.com/lgcollins/tr-maml

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

https://github.com/lgerrets/rl18-curiosity

Proximal Policy Optimization Algorithms

https://github.com/lh-wang/ACC

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/liamcli/darts

DARTS: Differentiable Architecture Search

https://github.com/liampetti/DDPG

Continuous control with deep reinforcement learning

https://github.com/lich14/CDS

Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

https://github.com/liernisestorain/zero-shot-dual-MT

Zero-Shot Dual Machine Translation

https://github.com/lif31up/model-agnostic-meta-learning

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

https://github.com/lightning-uq-box/lightning-uq-box

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

https://github.com/ligy2016/gym

OpenAI Gym

https://github.com/lil-lab/ciff

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

https://github.com/limxuanyu127/Tank-Game

Unity: A General Platform for Intelligent Agents

https://github.com/lina2360/HiSeqGan

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/linear95/tc-estimation

Representation Learning with Contrastive Predictive Coding

https://github.com/ling-pan/res

The StarCraft Multi-Agent Challenge

https://github.com/lingweizhu/Pytorch-MunchausenActorCritic

Soft Actor-Critic for Discrete Action Settings

https://github.com/linhlpv/Offline-and-OfflineOnline-RL

AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

https://github.com/lippman1125/darts_notes

DARTS: Differentiable Architecture Search

https://github.com/liqichen6688/baycnn

Weight Uncertainty in Neural Networks

https://github.com/lireer/ricochet-robot-solver

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/lit-leo/inner-convex-nn

Input Convex Neural Networks

https://github.com/liu-yushan/PoLo

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

https://github.com/liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow

Adversarial Learning for Neural Dialogue Generation

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