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Reinforcement Learning

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평가표

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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/aholliday/transit_learning

공식 코드

Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning

https://github.com/ahottung/EAS

공식 코드

Efficient Active Search for Combinatorial Optimization Problems

https://github.com/ai-for-decision-making-tue/drl-guided-picker-optimization

공식 코드

Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking

https://github.com/ai-initiative-kaust/videorlcs

공식 코드

Learning to Identify Critical States for Reinforcement Learning from Videos

https://github.com/ai-lab-science/deep-reinforcement-learning-for-mapless-navigation-in-intralogistics

공식 코드

Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics

https://github.com/ai-secure/crop

공식 코드

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

https://github.com/ai-wand/concise-reasoning

공식 코드

Concise Reasoning via Reinforcement Learning

https://github.com/ai4ce/SNAC

공식 코드

Simultaneous Navigation and Construction Benchmarking Environments

https://github.com/ai4co/rl4co

공식 코드

RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

https://github.com/ai4finance-foundation/elegantrl

공식 코드

ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning

https://github.com/ai4finance-foundation/finrl-meta

공식 코드

FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance

https://github.com/ai4pdlab/dpo_plm

공식 코드

Guiding Generative Protein Language Models with Reinforcement Learning

https://github.com/ai4science-westlakeu/diffphycon

공식 코드

DiffPhyCon: A Generative Approach to Control Complex Physical Systems

https://github.com/ai9stars/autotriton

공식 코드

AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs

https://github.com/aicenter/openspiel_reproductions

공식 코드

Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report

https://github.com/aidaafshar/learning-rate-free-reinforcement-learning

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Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives

https://github.com/aidefender/remern-remert

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Regret Minimization Experience Replay in Off-Policy Reinforcement Learning

https://github.com/ailabspace/drl-based-mapless-crowd-navigation-with-perceived-risk

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Deep Reinforcement Learning-Based Mapless Crowd Navigation with Perceived Risk of the Moving Crowd for Mobile Robots

https://github.com/aimagelab/focus-on-impact

공식 코드

Focus on Impact: Indoor Exploration with Intrinsic Motivation

https://github.com/ainfosec/codex

공식 코드

CODEX: A Cluster-Based Method for Explainable Reinforcement Learning

https://github.com/airi-institute/igor

공식 코드

Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environments

https://github.com/airi-institute/rate

공식 코드

Recurrent Action Transformer with Memory

https://github.com/airsplay/R2R-EnvDrop

공식 코드

Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout

https://github.com/ais-clemson/drl-bems

공식 코드

Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approach

https://github.com/ait-lab/ggtde

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Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning

https://github.com/aizazsharif/benchmarking-qrs-2022

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Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment

https://github.com/aj-correa/unraveling-the-rainbow

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Unraveling the Rainbow: can value-based methods schedule?

https://github.com/akifumi-wachi-4/safe_near_optimal_mdp

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Safe Reinforcement Learning in Constrained Markov Decision Processes

https://github.com/akjayant/mbppol

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Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm

https://github.com/akomandu/L1SVMIRL

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On the Correctness and Sample Complexity of Inverse Reinforcement Learning

https://github.com/akrouriad/tpami_metricrl

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Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts

https://github.com/akulen/alphagateau

공식 코드

Enhancing Chess Reinforcement Learning with Graph Representation

https://github.com/akuxcw/GridMask

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GridMask Data Augmentation

https://github.com/alaaselim-sudo/-rllib-impala

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Scalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach

https://github.com/aladoro/stabilizing-off-policy-rl

공식 코드

Stabilizing Off-Policy Deep Reinforcement Learning from Pixels

https://github.com/alaineman/drlcarsim-paper

공식 코드

Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers

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