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/bay3s/rl-squared

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

https://github.com/bayesian-optimization/bayesianoptimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/bchidamb/RL-Image-Classification

Natural Environment Benchmarks for Reinforcement Learning

https://github.com/bclyang/updated-atari-env

Surprising Negative Results for Generative Adversarial Tree Search

https://github.com/beauCoker/bayesian_neural_networks

Weight Uncertainty in Neural Networks

https://github.com/behzaad/Deep_QLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/behzadanksu/rlattack-dev

Noisy Networks for Exploration

https://github.com/belepi93/Ape-X

Distributed Prioritized Experience Replay

https://github.com/benevolentAI/guacamol_baselines

Proximal Policy Optimization Algorithms

https://github.com/benpetit/cs379c

Proximal Policy Optimization Algorithms

https://github.com/bentrevett/pytorch-rl

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/berkeleyflow/flow

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/betashort/ClassActivationMappings

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

https://github.com/bgLeon/EMG

Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement Learning

https://github.com/bgenchel/MusicalSeqGAN

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/bgenchel/Reinforcement-Learning-for-Music-Generation

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/bharadwaj1098/Tamer

Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces

https://github.com/bhiziroglu/Language-as-an-Abstraction-for-Hierarchical-Deep-Reinforcement-Learning

Language as an Abstraction for Hierarchical Deep Reinforcement Learning

https://github.com/bidimensional/Tennis

Continuous control with deep reinforcement learning

https://github.com/biemann/Collaboration-and-Competition

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

https://github.com/biemann/Continuous-Control

Continuous control with deep reinforcement learning

https://github.com/bilibili/LastOrder-Dota2

Dota 2 with Large Scale Deep Reinforcement Learning

https://github.com/bill86416/trojan_attack

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

https://github.com/billchan226/poar-srl-4-robot

Simple random search provides a competitive approach to reinforcement learning

https://github.com/biorobotics/PRD_environments

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

https://github.com/bitsauce/Carla-ppo

Learning to Drive in a Day

https://github.com/bitterbloom/Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/bj80heyue/Learning-to-Group

Merge or Not? Learning to Group Faces via Imitation Learning

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/bloomsburyai/question-generation

Machine Comprehension by Text-to-Text Neural Question Generation

https://github.com/bmeyers/VirtualMicrogridSegmentation

Continuous control with deep reinforcement learning

https://github.com/bo-pang/OLSPI

Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems

https://github.com/bonniesjli/DQN_SR

Count-Based Exploration with the Successor Representation

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