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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/2023-MindSpore-1/ms-code-220/tree/main/pmdb

Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief

https://github.com/2023-MindSpore-1/ms-code-221/tree/main/qmix

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/2023-MindSpore-1/ms-code-224/tree/main/adelaide_ea

Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

https://github.com/2023-MindSpore-1/ms-code-52

Playing Atari with Deep Reinforcement Learning

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/TCN

Time-Contrastive Networks: Self-Supervised Learning from Video

https://github.com/2023-MindSpore-4/Code-5/tree/main/MAML

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

https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_11_optimization

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/2023-MindSpore-4/Code13/tree/main/zhanglan/MLink-main

MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference

https://github.com/2023-MindSpore-4/Code13/tree/main/zhanglan/MLink-main/mlink_ms

MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference

https://github.com/2023-MindSpore-4/Code14/tree/main/RNA

Learning to Design RNA

https://github.com/2023-MindSpore-4/Code6/tree/main/RNA

Learning to Design RNA

https://github.com/2023-MindSpore-4/Code8/tree/main/AutoSlim

AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/2024-MindSpore-1/Code6/tree/main/AutoSlim

AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

https://github.com/2855239858/CenLight-Traffic-Grid-Signal-Optimization-viaAction-and-State-Decomposition

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/2mawi2/master-thesis-experiments

Proximal Policy Optimization Algorithms

https://github.com/329tyson/fai-ssl-challenge

Scaling and Benchmarking Self-Supervised Visual Representation Learning

https://github.com/3neutronstar/traffic-optimization_RL

Learning Phase Competition for Traffic Signal Control

https://github.com/4rChon/NL-FuN

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/5410tiffany/EIIE-keras

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/62subinh/deeprl_safety_specification

Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach

https://github.com/7starsea/Prioritized-Experience-Replay

Prioritized Experience Replay

https://github.com/ABKGroup/PlacementEssentialReadings

Chip Placement with Deep Reinforcement Learning

https://github.com/ACampero/dopamine

Prioritized Experience Replay

https://github.com/ACampero/hsp

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

https://github.com/ADGEfficiency/dsr_rl

Deep Reinforcement Learning: An Overview

https://github.com/AI-RG/rl-experiments

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/AI4Finance-LLC/FinRL

Practical Deep Reinforcement Learning Approach for Stock Trading

https://github.com/AI4Finance-LLC/FinRL-Library

Practical Deep Reinforcement Learning Approach for Stock Trading

https://github.com/AI4Finance-LLC/Liquidation-Analysis-using-Multi-Agent-Reinforcement-Learning-ICML-2019

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis

https://github.com/AIDefender/MyDiscor

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

https://github.com/AIJoris/DPAC-DialogueGAN

Adversarial Learning for Neural Dialogue Generation

https://github.com/AIRLegend/ChessRL

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

https://github.com/AMDonati/RL_Deep-RL_AM_Leandro

Learning to Understand Goal Specifications by Modelling Reward

https://github.com/AMR-/Conservative-Q-Improvement

Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy

https://github.com/ARISE-Initiative/robomimic

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

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