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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://github.com/stanford-iliad/teaching

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Assistive Teaching of Motor Control Tasks to Humans

https://github.com/stanford-iprl-lab/multimodal_representation

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Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

https://github.com/stanfordmlgroup/MedSelect

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MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning

https://github.com/stanfordnlp/wge

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Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

https://github.com/stanl1y/tdil

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Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning

https://github.com/statisticalreinforcementlearninglab/miwaves_rl_service

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reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use

https://github.com/statisticalreinforcementlearninglab/oralytics_reward_design

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Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care

https://github.com/statisticalreinforcementlearninglab/pcs-for-rl

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Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines

https://github.com/stavrosorf/dt4evs

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GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments

https://github.com/stavrosorf/ev2gym

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EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking

https://github.com/steamerlee/masa

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Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management

https://github.com/stefanosantaris/melanie

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Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events

https://github.com/stefanosantaris/sar

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Sequence Adaptation via Reinforcement Learning in Recommender Systems

https://github.com/steffengra/2310_beamforming_learner_2

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Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement Learning

https://github.com/stellacydong/Integrating-Generative-Models-and-Reinforcement-Learning-for-Reinsurance-Optimization

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A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement Learning

https://github.com/stephen-chung-mh/weight_max

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Learning by Competition of Self-Interested Reinforcement Learning Agents

https://github.com/stepjam/ARM

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Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation

https://github.com/stepjam/BPP

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Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning

https://github.com/stepjam/PyRep

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PyRep: Bringing V-REP to Deep Robot Learning

https://github.com/stevezhangza/adverserial_density_regression

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Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted Regression

https://github.com/sticky-mittens/code

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Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates

https://github.com/stiglidu/autotruss

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Automatic Truss Design with Reinforcement Learning

https://github.com/stillwolf/acsf-snn

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A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning

https://github.com/stilwell-git/adaptation-with-noisy-oracle

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Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation

https://github.com/stilwell-git/randomized-return-decomposition

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Learning Long-Term Reward Redistribution via Randomized Return Decomposition

https://github.com/stonet2000/rfcl

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Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement Learning

https://github.com/strath-ace/smart-ml

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A Novel Update Mechanism for Q-Networks Based On Extreme Learning Machines

https://github.com/struplm/udrl-gcsl-counterexample

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Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets

https://github.com/stwerner97/pdecontrol

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Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs

https://github.com/suber-team/suber

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SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems

https://github.com/submission-conf/neurips_cooperativeai

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Tackling Asymmetric and Circular Sequential Social Dilemmas with Reinforcement Learning and Graph-based Tit-for-Tat

https://github.com/success-muri/counterfactual_human_irl

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Reasoning about Counterfactuals to Improve Human Inverse Reinforcement Learning

https://github.com/success-muri/counterfactual_human_irl_study

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Reasoning about Counterfactuals to Improve Human Inverse Reinforcement Learning

https://github.com/sufe-aiflm-lab/fin-r1

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Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning

https://github.com/suguman/nonintegergames

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Synthesis from Satisficing and Temporal Goals

https://github.com/sujoyp/subgoal-discovery

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Learning from Trajectories via Subgoal Discovery

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