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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/rpm1412/lus_video_summarization

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Unsupervised multi-latent space reinforcement learning framework for video summarization in ultrasound imaging

https://github.com/rraileanu/auto-drac

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Automatic Data Augmentation for Generalization in Deep Reinforcement Learning

https://github.com/rraileanu/idaac

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Decoupling Value and Policy for Generalization in Reinforcement Learning

https://github.com/rsantana-isg/morl_benchmark

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Benchmarking MOEAs for solving continuous multi-objective RL problems

https://github.com/rschiewer/lrdm

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Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning

https://github.com/rssalessio/optimal-attack-control-channel-mdp

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Balancing detectability and performance of attacks on the control channel of Markov Decision Processes

https://github.com/rstrudel/rlbc

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Learning to combine primitive skills: A step towards versatile robotic manipulation

https://github.com/rubai5/ESS_Game

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Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?

https://github.com/rubenrtorrado/GVGAI_GYM

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Deep Reinforcement Learning for General Video Game AI

https://github.com/rucaibox/cir

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Towards Effective Code-Integrated Reasoning

https://github.com/rucaibox/rlmec

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Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint

https://github.com/ruihuang1967/Deep-GRAIL

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Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR Streaming

https://github.com/ruizhaogit/EnergyBasedPrioritization

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Energy-Based Hindsight Experience Prioritization

https://github.com/ruizhaogit/GuessWhat-TemperedPolicyGradient

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Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient

https://github.com/ruizhaogit/MNIST-GuessNumber

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Efficient Dialog Policy Learning via Positive Memory Retention

https://github.com/ruizhaogit/PositiveMemoryRetention

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Efficient Dialog Policy Learning via Positive Memory Retention

https://github.com/ruizhaogit/maximum_entropy_population_based_training

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Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination

https://github.com/ruizhaogit/mep

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Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

https://github.com/runamu/compositional-conservatism

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Compositional Conservatism: A Transductive Approach in Offline Reinforcement Learning

https://github.com/runzhestat/causalmarl

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A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets

https://github.com/ruohanzhan/rl_inference

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Post Reinforcement Learning Inference

https://github.com/ruohliuq/c-morl

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C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front

https://github.com/ruoqizzz/entropy-offlineRL

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Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning

https://github.com/ruotianluo/coco-caption

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Analysis of diversity-accuracy tradeoff in image captioning

https://github.com/russellmendonca/maesn_suite

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Meta-Reinforcement Learning of Structured Exploration Strategies

https://github.com/rwickman/sparrl-pytorch

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A Generic Graph Sparsification Framework using Deep Reinforcement Learning

https://github.com/rwth-iaew/rrl-voltage-control

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Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids

https://github.com/ryanshea10/personachat_offline_rl

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Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning

https://github.com/ryanxhr/dwbc

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Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations

https://github.com/ryanxhr/ivr

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Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization

https://github.com/ryanxhr/por

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A Policy-Guided Imitation Approach for Offline Reinforcement Learning

https://github.com/s-marton/sympol

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Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization

https://github.com/s-sd/spurl

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Competing for pixels: a self-play algorithm for weakly-supervised segmentation

https://github.com/s-sd/task-amenability

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Image quality assessment for machine learning tasks using meta-reinforcement learning

https://github.com/s-tian/mbold

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Model-Based Visual Planning with Self-Supervised Functional Distances

https://github.com/sa-and/causalplayground

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CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research

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