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Reinforcement Learning (RL)

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하위 태스크: 6
평가표: 2
보존된 평가 기록: 3

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/SilentEchoes77/hybrid-transfer-rl

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Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data

https://github.com/Skylark0924/TendonTrack_PILCO

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Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge

https://github.com/SliverySky/mariopuzzle

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Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study

https://github.com/SmartData-Polito/RL-IoT

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RL-IoT: Reinforcement Learning to Interact with IoT Devices

https://github.com/SobhanMP/decoupled-soft-RL

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Decoupling regularization from the action space

https://github.com/SobihanSurendran/LGS

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Latent Guided Sampling for Combinatorial Optimization

https://github.com/Sohojoe/ppo-dash

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PPO Dash: Improving Generalization in Deep Reinforcement Learning

https://github.com/Sonkyunghwan/QTRAN

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QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

https://github.com/SonyAI/PandemicSimulator

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Reinforcement Learning for Optimization of COVID-19 Mitigation policies

https://github.com/Space-Robotics-Laboratory/rlstar

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RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots

https://github.com/Spider-scnu/TSP

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Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances

https://github.com/Stanford-ILIAD/CARLO

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Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving

https://github.com/Stanford-ILIAD/PantheonRL

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PantheonRL: A MARL Library for Dynamic Training Interactions

https://github.com/StanfordAI4HI/CSRL

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Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning

https://github.com/StanfordASL/BaRC

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BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning

https://github.com/StanfordASL/RSIRL

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Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods

https://github.com/StatNLP/mt-correct-mark-interface

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Correct Me If You Can: Learning from Error Corrections and Markings

https://github.com/StatisticalReinforcementLearningLab/oralytics_algorithm_design

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Oralytics Reinforcement Learning Algorithm

https://github.com/StatisticalReinforcementLearningLab/oralytics_pilot_data

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Oralytics Reinforcement Learning Algorithm

https://github.com/Stepan-Makarenko/RL_interferometer_alignment

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Aligning an optical interferometer with beam divergence control and continuous action space

https://github.com/Stilwell-Git/Hindsight-Goal-Generation

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Exploration via Hindsight Goal Generation

https://github.com/StoneT2000/trajectorytranslation

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Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization

https://github.com/SuReLI/llrl

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Lipschitz Lifelong Reinforcement Learning

https://github.com/SuerpX/Embedded-Self-Predictions

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Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions

https://github.com/SunwardTree/TDDPG-Rec

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A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation

https://github.com/SvenGronauer/phoenix-drone-simulation

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Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors

https://github.com/TEA-Lab/diffusion_reward

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Diffusion Reward: Learning Rewards via Conditional Video Diffusion

https://github.com/THUDM/LongReward

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LongReward: Improving Long-context Large Language Models with AI Feedback

https://github.com/TIERS/NoisyKukaReacher

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Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning

https://github.com/TILOS-AI-Institute/MacroPlacement

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Assessment of Reinforcement Learning for Macro Placement

https://github.com/TJU-DRL-LAB/self-supervised-rl/tree/ece95621b8c49f154f96cf7d395b95362a3b3d4e/RL_with_Environment_Representation/ccm

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Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning

https://github.com/TSummersLab/polgrad-multinoise

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Learning robust control for LQR systems with multiplicative noise via policy gradient

https://github.com/TU2021/DPO-VP

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Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

https://github.com/TZW1998/Taming-Stable-Diffusion-with-Human-Ranking-Feedback

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Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles

https://github.com/TakaOsa/adInfoHRL

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Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization

https://github.com/TakuyaHiraoka/Dropout-Q-Functions-for-Doubly-Efficient-Reinforcement-Learning

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Dropout Q-Functions for Doubly Efficient Reinforcement Learning

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