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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/sunyasheng/a2rl_pytorch

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

https://github.com/super5-22/habitat-vis

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

https://github.com/suragnair/alpha-zero-general

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/suragnair/seqGAN

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/suryakmurthy/uam_noise_aware_agent

A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance

https://github.com/suryakmurthy/uam_shared_scheduling

A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance

https://github.com/susan-amin/SparseBaseline1

Continuous control with deep reinforcement learning

https://github.com/susanwe/ope_worldmodels

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/susanzhang233/mollykill

MolGAN: An implicit generative model for small molecular graphs

https://github.com/susumuota/distributed_experience_replay

RLlib: Abstractions for Distributed Reinforcement Learning

https://github.com/susumuota/gym-modeestimation

Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning

https://github.com/susumuota/gym-oculoenv

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents

https://github.com/suvhradipghosh07/Augmented-Random-Search

Simple random search provides a competitive approach to reinforcement learning

https://github.com/svikramank/chess-deepRL

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/svikramank/dm_control

DeepMind Control Suite

https://github.com/swordest/mec_drl

Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach

https://github.com/swordgeek/SR

Searching for Activation Functions

https://github.com/sygi/deep_q_rl

Playing Atari with Deep Reinforcement Learning

https://github.com/syigzaw/WorldModels

World Models

https://github.com/synthlabsai/big-math

Proximal Policy Optimization Algorithms

https://github.com/t3p/baselines

Policy Optimization via Importance Sampling

https://github.com/taesiri/my-mlagents-envs

Unity: A General Platform for Intelligent Agents

https://github.com/tahanakabi/Deep-Reinforcenment-learning-for-TCL-control

Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control

https://github.com/tajwarfahim/proactive_interventions

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/taku-y/20181125-pybullet

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/takuseno/ppo

Proximal Policy Optimization Algorithms

https://github.com/takuseno/unreal

Reinforcement Learning with Unsupervised Auxiliary Tasks

https://github.com/talipucar/PyFlow_CPC

Representation Learning with Contrastive Predictive Coding

https://github.com/tally0818/DARTS

DARTS: Differentiable Architecture Search

https://github.com/tally0818/NASNet

Neural Architecture Search with Reinforcement Learning

https://github.com/tanayz/TC-Bot-py3

A User Simulator for Task-Completion Dialogues

https://github.com/tangxiangru/RL-for-RNA-design

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/tanjimin/grad-cam-pytorch-light

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

https://github.com/tanyuqian/progressive-generation

Toward Diverse Text Generation with Inverse Reinforcement Learning

https://github.com/taodav/TextWorldACG

Towards Solving Text-based Games by Producing Adaptive Action Spaces

https://github.com/tarod13/SAC

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

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