Papers with Code 아카이브

Reinforcement Learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/reinforceio/tensorforce

공식 코드

LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations

https://github.com/rekriz11/sockeye-recipes

공식 코드

Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification

https://github.com/remosasso/psdrl

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Posterior Sampling for Deep Reinforcement Learning

https://github.com/rems75/SPIBB-DQN

공식 코드

Safe Policy Improvement with Baseline Bootstrapping

https://github.com/renming-huang/trajectorydiffuser

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Diffusion Models as Optimizers for Efficient Planning in Offline RL

https://github.com/renqianluo/NAO

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Neural Architecture Optimization

https://github.com/renqianluo/NAO_pytorch

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Neural Architecture Optimization

https://github.com/renweiya/RFQ-RFAC

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Represented Value Function Approach for Large Scale Multi Agent Reinforcement Learning

https://github.com/replicable-marl/marllib

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MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library

https://github.com/researcher-tiger/moepg

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Reinforcement Learning Guided Multi-Objective Exam Paper Generation

https://github.com/resibots/blackdrops

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Black-Box Data-efficient Policy Search for Robotics

https://github.com/resibots/chatzilygeroudis_2018_rte

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Reset-free Trial-and-Error Learning for Robot Damage Recovery

https://github.com/resibots/kaushik_2018_multi-dex

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Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards

https://github.com/resibots/kaushik_2020_famle

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Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors

https://github.com/revorg7/DeepSparseSampling

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Bayesian Reinforcement Learning via Deep, Sparse Sampling

https://github.com/rewardreports/reward-reports

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Reward Reports for Reinforcement Learning

https://github.com/rezabonyadi/atari_rl_arxiv

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Self Punishment and Reward Backfill for Deep Q-Learning

https://github.com/rezakakooee/rldesigner

공식 코드

Design Process is a Reinforcement Learning Problem

https://github.com/rfuruta/pixelRL

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PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing

https://github.com/rgsmirnov/cfg_safety_llm

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Classifier-free guidance in LLMs Safety

https://github.com/rhoowd/sched_net

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Learning to Schedule Communication in Multi-agent Reinforcement Learning

https://github.com/rhr407/relevagan

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Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection

https://github.com/ricarddurall/benchmarking-strategies-for-asset-allocation

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Asset Allocation: From Markowitz to Deep Reinforcement Learning

https://github.com/riccardopoiani/trio-non-stationary-meta-rl

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Meta-Reinforcement Learning by Tracking Task Non-stationarity

https://github.com/ricgama/maenvs4vrp

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Multi-Agent Environments for Vehicle Routing Problems

https://github.com/ricky-zhu/irdec

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Learning to Solve Tasks with Exploring Prior Behaviours

https://github.com/rickyang2016/bayesian-soft-actor-critic--bsac

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Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement Learning

https://github.com/ricsonc/ActiveVisionManipulation

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Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions

https://github.com/riflezhang/llava-reasoner-dpo

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Improve Vision Language Model Chain-of-thought Reasoning

https://github.com/ringbdstack/drl-dbscan

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Automating DBSCAN via Deep Reinforcement Learning

https://github.com/rishatzagidullin/nlse-rl

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Suppressing Modulation Instability with Reinforcement Learning

https://github.com/rishavb123/EMMA

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External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling

https://github.com/riveSunder/carle

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Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity

https://github.com/river-lab/mobiman

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Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning

https://github.com/rizar/actor-critic-public

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An Actor-Critic Algorithm for Sequence Prediction

https://github.com/rl-boxes/safe-rl

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CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning

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