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Reinforcement Learning

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보존된 평가 기록

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코드

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

https://github.com/instadeepai/jumanji

Podracer architectures for scalable Reinforcement Learning

https://github.com/instadeepai/marl-eval

Deep Reinforcement Learning at the Edge of the Statistical Precipice

https://github.com/instadeepai/memento

Efficient Active Search for Combinatorial Optimization Problems

https://github.com/instadeepai/poppy

Efficient Active Search for Combinatorial Optimization Problems

https://github.com/intelligent-environments-lab/CityLearn

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/intellisys-lab/stellaris-sc24

Proximal Policy Optimization Algorithms

https://github.com/intenseG/BSK

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

https://github.com/invictos/InsacarDQN

Playing Atari with Deep Reinforcement Learning

https://github.com/ir2718/semantic-similarity-scoring

Population Based Training of Neural Networks

https://github.com/ironbar/AnimalAI-Olympics

Unity: A General Platform for Intelligent Agents

https://github.com/irskid5/drr_restaurants

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

https://github.com/isaaccasm/GradCAM-keras

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

https://github.com/islaterm/you-activated-my-neuron

Searching for Activation Functions

https://github.com/ismailuddin/gradcam-tensorflow-2

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

https://github.com/isp1tze/MAProj

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

https://github.com/ist-daslab/rosa

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/istar1978/plato

Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning

https://github.com/itanvir/gradcam

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

https://github.com/ito-rafael/once-for-all-2

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

https://github.com/iusztinpaul/portfolio-management

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/ivanfeliciano/AnimalAI-INAOE

Unity: A General Platform for Intelligent Agents

https://github.com/iwangjian/textsum-gan

Generative Adversarial Network for Abstractive Text Summarization

https://github.com/jaara/classification-with-costly-features

Classification with Costly Features as a Sequential Decision-Making Problem

https://github.com/jachiam/cpo

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/jackaduma/Alpaca-LoRA-RLHF-PyTorch

Fine-Tuning Language Models from Human Preferences

https://github.com/jacksonchen1998/Cold-Start-Reinforcement-Learning-with-Softmax-Policy-Gradient

Cold-Start Reinforcement Learning with Softmax Policy Gradient

https://github.com/jackyhum/NeuralOperatorFeedbackWithSpatiallyVaryingStateDelay

Neural Operator based Reinforcement Learning for Control of first-order PDEs with Spatially-Varying State Delay

https://github.com/jacobgil/keras-grad-cam

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

https://github.com/jacobgil/pytorch-grad-cam

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

https://github.com/jacobkrantz/VLN-CE

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/jadag/DDQN_mario

Deep Reinforcement Learning with Double Q-learning

https://github.com/jadag/a2cTrader

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/jadag/trader

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/jaem-seo/AI_tokamak_control

Hindsight Experience Replay

https://github.com/jaesik817/bmaml

Bayesian Model-Agnostic Meta-Learning

https://github.com/jaesik817/bmaml_rl

Bayesian Model-Agnostic Meta-Learning

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