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/jainpankul/Augmented-Random-Search

Simple random search provides a competitive approach to reinforcement learning

https://github.com/jakegrigsby/deep_control

Continuous Deep Q-Learning with Model-based Acceleration

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/ddpg.py

Continuous control with deep reinforcement learning

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/grac.py

GRAC: Self-Guided and Self-Regularized Actor-Critic

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/sac.py

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

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/td3.py

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/jakegrigsby/supersonic

Exploration by Random Network Distillation

https://github.com/jakub-zarzycki2022/gym-pbn-stac

Deep Reinforcement Learning for Control of Probabilistic Boolean Networks

https://github.com/jangirrishabh/Overcoming-exploration-from-demos

Hindsight Experience Replay

https://github.com/jangirrishabh/gym

OpenAI Gym

https://github.com/janhuenermann/neurojs

Increasing the Action Gap: New Operators for Reinforcement Learning

https://github.com/jansenkeith501/CS295-MADDPG

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

https://github.com/jasonisgod/Big2

Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information

https://github.com/jasonrobwebster/alphazero-clone

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

https://github.com/jaylee0301/dhrl_official

DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning

https://github.com/jbinas/gym-mnist

Variational Intrinsic Control

https://github.com/jbrea/BayesianOptimization.jl

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

https://github.com/jchenghu/sacreeos

Self-critical Sequence Training for Image Captioning

https://github.com/jcoreyes/evolvingrl

Evolving Reinforcement Learning Algorithms

https://github.com/jcoreyes/ex2

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

https://github.com/jcoreyes/lgpl

Guiding Policies with Language via Meta-Learning

https://github.com/jcwleo/curiosity-driven-exploration-pytorch

Efficient Parallel Methods for Deep Reinforcement Learning

https://github.com/jcwleo/random-network-distillation-pytorch

Efficient Parallel Methods for Deep Reinforcement Learning

https://github.com/jeanharb/option_critic

The Option-Critic Architecture

https://github.com/jeapostrophe/monaco

A0C: Alpha Zero in Continuous Action Space

https://github.com/jeffchy/Artificial-Idiot

Playing FPS Games with Deep Reinforcement Learning

https://github.com/jeffery1236/Atari_DoubleDeepQNetwork

Deep Reinforcement Learning with Double Q-learning

https://github.com/jefflai108/Contrastive-Predictive-Coding-PyTorch

Representation Learning with Contrastive Predictive Coding

https://github.com/jelleman8/TractSeg

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/jemaw/gym-safety

Constrained Policy Optimization

https://github.com/jeonghun3572/CoCoS

Training Language Models to Self-Correct via Reinforcement Learning

https://github.com/jerrylsu/TC-Bot

A User Simulator for Task-Completion Dialogues

https://github.com/jesbu1/carl

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

https://github.com/jeyoor/pymich-dl

Searching for Activation Functions

https://github.com/jezzarax/drlnd_p1_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/jfpettit/flare

Proximal Policy Optimization Algorithms

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