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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/xupei0610/KDMA

공식 코드

Human-Inspired Multi-Agent Navigation using Knowledge Distillation

https://github.com/xupei0610/PFPN

공식 코드

PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy Network

https://github.com/xushilin1/mixed-r1

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Mixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language Models

https://github.com/xuxiaoli-seu/SNARM-UAV-Learning

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Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning

https://github.com/xuyige/fgrl4kg

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Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning

https://github.com/xwhan/DeepPath

공식 코드

DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

https://github.com/xwhan/walk_the_blocks

공식 코드

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

https://github.com/xybfight/nar4tsp

공식 코드

Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems

https://github.com/xylee95/Spatiotemporal-Attack-On-Deep-RL-Agents

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Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents

https://github.com/xylee95/md-pgt

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MDPGT: Momentum-based Decentralized Policy Gradient Tracking

https://github.com/xylee95/targeted_adversarial_policies

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Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents

https://github.com/yan98/gta1

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GTA1: GUI Test-time Scaling Agent

https://github.com/yang-xy20/async_mappo

공식 코드

Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration

https://github.com/yang-xy20/mage-x

공식 코드

Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation

https://github.com/yangjenny/ImbalancedLearningRL

공식 코드

Deep Reinforcement Learning for Multi-class Imbalanced Training

https://github.com/yangjie-cv/wethink

공식 코드

WeThink: Toward General-purpose Vision-Language Reasoning via Reinforcement Learning

https://github.com/yangpuPKU/L2SR-Learning-to-Sample-and-Reconstruct

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L2SR: Learning to Sample and Reconstruct for Accelerated MRI via Reinforcement Learning

https://github.com/yangrui2015/awgcsl

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Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL

https://github.com/yangrui2015/ric

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Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment

https://github.com/yangrui2015/riql

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Towards Robust Offline Reinforcement Learning under Diverse Data Corruption

https://github.com/yangrui2015/rorl

공식 코드

RORL: Robust Offline Reinforcement Learning via Conservative Smoothing

https://github.com/yangrui2015/uwmsg

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Corruption-Robust Offline Reinforcement Learning with General Function Approximation

https://github.com/yangysc/resinet

공식 코드

Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features

https://github.com/yangzhao1230/taco

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Regulatory DNA sequence Design with Reinforcement Learning

https://github.com/yannikkellerde/gnn_hex

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From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?

https://github.com/yannkerzreho/markovgameapproximation

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Homogenization of Multi-agent Learning Dynamics in Finite-state Markov Games

https://github.com/yanshengyuan/Data-driven-approach-towards-more-efficient-Newton-Raphson-power-flow-calculation

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Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution grids

https://github.com/yantianzha/serlfd

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Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning

https://github.com/yaotang23/currmask

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Learning Versatile Skills with Curriculum Masking

https://github.com/yaqianzhang/repeatedaugmentedrehearsal

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A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

https://github.com/yaqingwang/WeFEND-AAAI20

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Weak Supervision for Fake News Detection via Reinforcement Learning

https://github.com/yardenas/la-mbda

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Constrained Policy Optimization via Bayesian World Models

https://github.com/yaserkl/RLSeq2Seq

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Deep Reinforcement Learning For Sequence to Sequence Models

https://github.com/yaserkl/TransferRL

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Deep Transfer Reinforcement Learning for Text Summarization

https://github.com/yashchandak/UnO

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Universal Off-Policy Evaluation

https://github.com/yashchandak/activens

공식 코드

Off-Policy Evaluation for Action-Dependent Non-Stationary Environments

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