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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/luofuli/DualLanST

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A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer

https://github.com/lupoglaz/TorchProteinLibrary

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TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure

https://github.com/lviano/identifiability_irl

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Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning

https://github.com/lviano/robust_gaifo

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Robust Learning from Observation with Model Misspecification

https://github.com/lviano/robustmce_irl

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Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

https://github.com/lyeskhalil/corl

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Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach

https://github.com/lyingCS/UOEP

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UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems

https://github.com/lz1oceani/pointcloud_rl

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On the Efficacy of 3D Point Cloud Reinforcement Learning

https://github.com/machado-research/AgarCL

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The Cell Must Go On: Agar.io for Continual Reinforcement Learning

https://github.com/machelreid/can-wikipedia-help-offline-rl

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Can Wikipedia Help Offline Reinforcement Learning?

https://github.com/machine-teaching-group/ijcai2024-proximal-curriculum-target-rl

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Proximal Curriculum with Task Correlations for Deep Reinforcement Learning

https://github.com/machine-teaching-group/tmlr2023_proximal-curriculum-rl

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Proximal Curriculum for Reinforcement Learning Agents

https://github.com/madoibito80/im2sem

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Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning

https://github.com/maestro-project/confuciux

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ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning

https://github.com/magkai/CONQUER

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Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs

https://github.com/magnetar-iiith/pril

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How Private Is Your RL Policy? An Inverse RL Based Analysis Framework

https://github.com/mahaitongdae/Feasible-Actor-Critic

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Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety

https://github.com/mahaitongdae/Reachability_Constrained_RL

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Reachability Constrained Reinforcement Learning

https://github.com/mahaitongdae/safe_exp_env

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Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function

https://github.com/mahaitongdae/safety_index_synthesis

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Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning

https://github.com/mahossam/OptiGAN

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OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

https://github.com/mail-ecnu/vmagent

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VMAgent: Scheduling Simulator for Reinforcement Learning

https://github.com/maitchison/PPO

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DNA: Proximal Policy Optimization with a Dual Network Architecture

https://github.com/malhotra-sidharth/game-of-ur-reinforcement-learning

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Solving Royal Game of Ur Using Reinforcement Learning

https://github.com/malteschilling/ddrl_hexapod

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Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot

https://github.com/mamba413/room

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Robust Offline Reinforcement learning with Heavy-Tailed Rewards

https://github.com/manantomar/Mirror-Descent-Policy-Optimization

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Mirror Descent Policy Optimization

https://github.com/manantomar/agent-centric-representations

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Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information

https://github.com/mandareln/deep-q-learning-adversarial

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Deep Q learning for fooling neural networks

https://github.com/manga-uofa/lmreward

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Teacher Forcing Recovers Reward Functions for Text Generation

https://github.com/manish-pra/non-additive-rl

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Submodular Reinforcement Learning

https://github.com/mansicer/background-knowledge-rl

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Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Models

https://github.com/maohangyu/TIT_open_source

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Transformer in Transformer as Backbone for Deep Reinforcement Learning

https://github.com/maohangyu/pdit

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PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning

https://github.com/maoliyuan/odice-pytorch

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ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update

https://github.com/maoxiaowei97/drl4route

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DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction

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