Papers with Code 아카이브

Reinforcement Learning (RL)

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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

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

https://github.com/ilonatommy/DLR_FacialEmotionRecognition

Image Classification by Reinforcement Learning with Two-State Q-Learning

https://github.com/imatge-upc/progressive_nns

Progressive Neural Networks

https://github.com/impredicative/irc-rss-feed-bot

Concurrent Meta Reinforcement Learning

https://github.com/in1t3r/ARS

Simple random search provides a competitive approach to reinforcement learning

https://github.com/inclusionai/aworld

RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism

https://github.com/indrajeet95/Device-Placement-Optimization-with-Reinforcement-Learning

Device Placement Optimization with Reinforcement Learning

https://github.com/infinitemugen/MAML-Pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/inoryy/reaver

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/insomnia94/ARG

Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning

https://github.com/instadeepai/fastpbrl

Fast Population-Based Reinforcement Learning on a Single Machine

https://github.com/instadeepai/flashbax

Prioritized Experience Replay

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

Deep Reinforcement Learning at the Edge of the Statistical Precipice

https://github.com/instadeepai/poppy

Efficient Active Search for Combinatorial Optimization Problems

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/irom-princeton/dppo

Diffusion Policy Policy Optimization

https://github.com/irskid5/drr_restaurants

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

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/iusztinpaul/portfolio-management

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

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/jackieli19/pgportfolio

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

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/jacobkooi/hadamax

Rainbow: Combining Improvements in Deep Reinforcement Learning

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/jainpankul/Augmented-Random-Search

Simple random search provides a competitive approach to reinforcement learning

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

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