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/timoklein/redo

The Dormant Neuron Phenomenon in Deep Reinforcement Learning

https://github.com/tinhb92/rnn_darts_fastai

DARTS: Differentiable Architecture Search

https://github.com/titardrew/assistive-gym

Assistive Gym: A Physics Simulation Framework for Assistive Robotics

https://github.com/titu1994/neural-architecture-search

Neural Architecture Search with Reinforcement Learning

https://github.com/titu1994/progressive-neural-architecture-search

Progressive Neural Architecture Search

https://github.com/tiwarikajal/Seq2SQL-

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/tjcdev/mlpgym

OpenAI Gym

https://github.com/tju-drl-lab/self-supervised-rl

PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations

https://github.com/tjuhaoxiaotian/pymarl3

Value-Decomposition Networks For Cooperative Multi-Agent Learning

https://github.com/tkarr21/multagent-particle-envs

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

https://github.com/tkarr21/multiagent-particle-envs

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

https://github.com/tkcoding/DeepRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/tkcoding/Stock_DRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/tlohr/nfsu2-ai

Playing Atari with Deep Reinforcement Learning

https://github.com/tmjeong1103/RL_with_RAY

Proximal Policy Optimization Algorithms

https://github.com/tmoer/a0c

A0C: Alpha Zero in Continuous Action Space

https://github.com/tobiasemrich/SchafkopfRL

Proximal Policy Optimization Algorithms

https://github.com/tobiasvanderwerff/MetaHTR

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

https://github.com/tomaszsmaruj25/Twin-Delayed-DDPG-Implementation

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/tommyvsfu1/RL-NTU

Proximal Policy Optimization Algorithms

https://github.com/toniesteves/cnn-bayesian-optimization

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

https://github.com/tophatraptor/si-transfer

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

https://github.com/torchjd/torchjd

Gradient Surgery for Multi-Task Learning

https://github.com/toshikwa/discor.pytorch

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

https://github.com/toshikwa/slac.pytorch

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/toshikwa/soft-actor-critic.pytorch

Prioritized Experience Replay

https://github.com/toybox-rs/Toybox

ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents

https://github.com/toybox-rs/openai-baselines-envs

ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents

https://github.com/tphanson/xupr-drl

Prioritized Experience Replay

https://github.com/tr7200/Vadam

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

https://github.com/traai/async-deep-rl

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/trackmania-rl/tmrl

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

https://github.com/trailofbits/privacyraven

Knockoff Nets: Stealing Functionality of Black-Box Models

https://github.com/transedward/pytorch-hdqn

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

https://github.com/trisongz/question-answering-lightning

Machine Comprehension by Text-to-Text Neural Question Generation

https://github.com/tristandeleu/gfn-maxent-rl

Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions

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