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Reinforcement Learning (RL)

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하위 태스크: 6
평가표: 2
보존된 평가 기록: 3

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/weiwang-wys/irsconfigurationdrl

공식 코드

Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement Learning

https://github.com/weizhepei/webagent-r1

공식 코드

WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning

https://github.com/wendelinboehmer/dcg

공식 코드

Deep Coordination Graphs

https://github.com/wenqi-cui/trajectory-generation

공식 코드

Efficient Reinforcement Learning Through Trajectory Generation

https://github.com/wenshuaizhao/spmarl

공식 코드

Learning Progress Driven Multi-Agent Curriculum

https://github.com/wh1992v/ggp-rl

공식 코드

Assessing the Potential of Classical Q-learning in General Game Playing

https://github.com/whaleloops/interpoetry

공식 코드

Generating Classical Chinese Poems from Vernacular Chinese

https://github.com/whitemech/markov-abstractions-code-ijcai22

공식 코드

Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision Processes

https://github.com/wil3/gymfc

공식 코드

Flight Controller Synthesis Via Deep Reinforcement Learning

https://github.com/wil3/neuroflight

공식 코드

Flight Controller Synthesis Via Deep Reinforcement Learning

https://github.com/william-hayes/llms-biased-rl

공식 코드

Large Language Models are Biased Reinforcement Learners

https://github.com/williambug/event_driven_rl

공식 코드

Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes

https://github.com/wilrop/ipro

공식 코드

Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning

https://github.com/wilson1yan/rlpyt

공식 코드

Learning to Manipulate Deformable Objects without Demonstrations

https://github.com/windylee0822/ctg

공식 코드

Reinforcement Learning with Token-level Feedback for Controllable Text Generation

https://github.com/winninghealth/WiNGPT3

공식 코드

WiNGPT-3.0 Technical Report

https://github.com/wjn1996/PCNN_RL_HME

공식 코드

RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational Searching

https://github.com/wjn1996/prejudge-before-think

공식 코드

Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge Reasoning

https://github.com/wkh923/m3pc

공식 코드

M$^3$PC: Test-time Model Predictive Control for Pretrained Masked Trajectory Model

https://github.com/wleiiiii/gym-atc-attack-project

공식 코드

Explainable and Safe Reinforcement Learning for Autonomous Air Mobility

https://github.com/wmn7/universal_light

공식 코드

UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control

https://github.com/wogwan/ccta2022_saferl

공식 코드

Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement Learning

https://github.com/wonjoon-yun/quantum-multi-agent-reinforcement-learning

공식 코드

Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design

https://github.com/woonsangcho/contrast_qgen

공식 코드

Contrastive Multi-document Question Generation

https://github.com/woooodyy/llm-reverse-curriculum-rl

공식 코드

Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning

https://github.com/woqingdoua/imbalanceclass

공식 코드

Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification

https://github.com/workharduwillwin/Depth-Self-Optimized-Learning-Toward-Data-Science

공식 코드

Depth Self-Optimized Learning Toward Data Science

https://github.com/wponghiran/lsm-rl

공식 코드

Reinforcement Learning with Low-Complexity Liquid State Machines

https://github.com/wq13552463699/real-robot-challenge-2022

공식 코드

Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation Policies

https://github.com/wuhuikai/TF-A2RL

공식 코드

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

https://github.com/wujingda/prioritized-human-in-the-loop-end-to-end-autonomous-driving

공식 코드

Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving

https://github.com/wulalago/AgentSPL

공식 코드

Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound

https://github.com/wumingqi/LLM-Math-Evaluation

공식 코드

Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination

https://github.com/wwydmanski/RLinWiFi

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Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning

https://github.com/wxy1427/learn-lns-policy

공식 코드

Learning Large Neighborhood Search Policy for Integer Programming

https://github.com/wyjung0625/p3s

공식 코드

Population-Guided Parallel Policy Search for Reinforcement Learning

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