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Reinforcement Learning (RL)

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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://gitlab.com/ComputationalScience/epidemic-control

공식 코드

Controlling epidemics through optimal allocation of test kits and vaccine doses across networks

https://gitlab.com/anonymousppocl_cm1/anonymous_collisions_paper

공식 코드

Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem

https://gitlab.com/ericavandersar/marl4powergridtopo

공식 코드

Multi-Agent Reinforcement Learning for Power Grid Topology Optimization

https://gitlab.com/hdonancio/pumpscheduling

공식 코드

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

https://gitlab.com/jwjwyoung/autopipeline-benchmarks

공식 코드

Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search

https://gitlab.com/kimon.protopapa/pmd-lookahead

공식 코드

Policy Mirror Descent with Lookahead

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/mit-mers/safe-visual-mapf

공식 코드

Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning

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

공식 코드

Urban Driving with Multi-Objective Deep Reinforcement Learning

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

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Bayesian Curiosity for Efficient Exploration in Reinforcement Learning

https://gitlab.com/tomblau/p2d2

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Learning from Demonstration without Demonstrations

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

공식 코드

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

https://gitlab.com/ymd_h/cpprb

공식 코드

ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

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

공식 코드

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

https://huggingface.co/mispeech/r1-aqa

공식 코드

Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question Answering

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

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

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

Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

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/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

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

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

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