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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://gitlab.com/hdonancio/pumpscheduling

공식 코드

The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning

https://gitlab.com/leocus/ge_q_dts

공식 코드

Evolutionary learning of interpretable decision trees

https://gitlab.com/lifelong-rl/lifelongRL_gridworld

공식 코드

Some Insights into Lifelong Reinforcement Learning Systems

https://gitlab.com/marvl-hipt/hipt

공식 코드

A Hierarchical Approach to Population Training for Human-AI Collaboration

https://gitlab.com/sumo-rl/sumo_openai_gym

공식 코드

Urban Driving with Multi-Objective Deep Reinforcement Learning

https://gitlab.com/tomblau/Bayesian-Curiosity

공식 코드

Bayesian Curiosity for Efficient Exploration in Reinforcement Learning

https://gitlab.com/wenlu057/traffic-safety

공식 코드

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

https://gitlab.com/xrl2/chat4xai

공식 코드

An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems

https://gitlab.nccr-automation.ch/loris.dinatale/NoDRL

공식 코드

Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control

https://research-git.uiowa.edu/sanzabizadeh/optimizing_warfarin_dosing_rev_1

공식 코드

Optimizing Warfarin Dosing using Deep Reinforcement Learning

https://worksheets.codalab.org/worksheets/0x0f25031bd42f4aabbc17625fe1484066

공식 코드

Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

https://worksheets.codalab.org/worksheets/0x453913e76b65495d8b9730d41c7e0a0c

공식 코드

Decoupling Strategy and Generation in Negotiation Dialogues

https://worksheets.codalab.org/worksheets/0x88c914ee1d4b4a4587a07f36f090f3e5

공식 코드

From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood

https://zenodo.org/record/4769612

공식 코드

Towards Scalable Verification of Deep Reinforcement Learning

https://bitbucket.org/skeizer/madrigal

Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling

https://github.com/09jvilla/CS234_gym

OpenAI Gym

https://github.com/0h-n0/weight_normalization

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/1110589721/openia-gym

OpenAI Gym

https://github.com/1230113202/NV-JM-DD

The Natural Language of Actions

https://github.com/15534081591/QMIX

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

https://github.com/16444take/aope-sim

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

https://github.com/170928/-Review-Dueling-Deep-Q-Network

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/170928/-Review-Generative-Adversarial-Imitation-Learning

Generative Adversarial Imitation Learning

https://github.com/170928/-Review-Proximal-Policy-Optimization-Algorithms

Proximal Policy Optimization Algorithms

https://github.com/19910101bacon/FinRL_v3

Practical Deep Reinforcement Learning Approach for Stock Trading

https://github.com/1hb6s7t/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/1hb6s7t/Rand-conv

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

https://github.com/1jsingh/rl_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/1nf0rmagician/gym-bimanualPickAndPlace

OpenAI Gym

https://github.com/2016choang/sfl

Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning

https://github.com/2023-MindSpore-1/ms-code-16/tree/main/adelaide_ea

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

https://github.com/2023-MindSpore-1/ms-code-17/tree/main/AutoSlim

AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DDAG

Continuous control with deep reinforcement learning

https://github.com/2023-MindSpore-1/ms-code-211/tree/main/DecoMR

DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pnasnet

Progressive Neural Architecture Search

https://github.com/2023-MindSpore-1/ms-code-218/tree/main/deepbsde

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

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