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Reinforcement Learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Nordeus/heroic-rl

공식 코드

Deep RL Agent for a Real-Time Action Strategy Game

https://github.com/OamIcs/MoSim

공식 코드

Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning

https://github.com/Octavio-Pappalardo/Meta-learning-rewards

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Black box meta-learning intrinsic rewards for sparse-reward environments

https://github.com/Ojig/Environment-Design-for-IRL

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Environment Design for Inverse Reinforcement Learning

https://github.com/OliverRichter/map-reader

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Teaching a Machine to Read Maps with Deep Reinforcement Learning

https://github.com/OpenLLMAI/OpenRLHF

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OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

https://github.com/OpenRL-Lab/DGPO

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DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization

https://github.com/OptiMaL-PSE-Lab/CIRL

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Control-Informed Reinforcement Learning for Chemical Processes

https://github.com/OptimalPursuit/navigating_uncertainty_in_mpp

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Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning

https://github.com/OscarHuangWind/Preference-Guided-DQN-Atari

공식 코드

Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration

https://github.com/Owen-Oertell/rlcm

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RL for Consistency Models: Faster Reward Guided Text-to-Image Generation

https://github.com/PAL-ML/PEARL_v1

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Pretrained Encoders are All You Need

https://github.com/PITT-JZ-COOP/Co-Explore-NAS

공식 코드

Hardware/Software Co-Exploration of Neural Architectures

https://github.com/PKU-AI-Edge/DGN

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Graph Convolutional Reinforcement Learning

https://github.com/PKU-AI-Edge/DGN/

공식 코드

Graph Convolutional Reinforcement Learning

https://github.com/PKU-AI-Edge/FEN

공식 코드

Learning Fairness in Multi-Agent Systems

https://github.com/PKU-RL/CLIP4MC

공식 코드

Reinforcement Learning Friendly Vision-Language Model for Minecraft

https://github.com/PPSantos/hybrid-marl

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Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning

https://github.com/PSVL/DoorGym

공식 코드

DoorGym: A Scalable Door Opening Environment And Baseline Agent

https://github.com/PTF-transfer/Code_PTF

공식 코드

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

https://github.com/PaLeroy/QVMix

공식 코드

QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning

https://github.com/PaddlePaddle/PaddleRobotics

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Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion

https://github.com/PaddlePaddle/XWorld

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Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents

https://github.com/PaddlePaddle/models

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Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment

https://github.com/PaladinEE15/RSAC

공식 코드

Robust Reinforcement Learning under model misspecification

https://github.com/PaoloP84/EfficacyModernES

공식 코드

Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization

https://github.com/PartnershipOnAI/safelife

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SafeLife 1.0: Exploring Side Effects in Complex Environments

https://github.com/Passerby/right-of-way-rl

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OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous Vehicles

https://github.com/PatrykChrabaszcz/Canonical_ES_Atari

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Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari

https://github.com/PaulEcoffet/RLCoopExp

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Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate with

https://github.com/Pavan-Samtani/RLSoccer

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Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination through Curriculum-Learning and Competition

https://github.com/Pearl-UTexas/ActiveVaR

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Risk-Aware Active Inverse Reinforcement Learning

https://github.com/PetropoulakisPanagiotis/igae

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State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping

https://github.com/PettingZoo-Team/SuperSuit

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SuperSuit: Simple Microwrappers for Reinforcement Learning Environments

https://github.com/Philip-Bachman/ICML-2015

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Variational Generative Stochastic Networks with Collaborative Shaping

https://github.com/PhilippeMorere/learning-to-plan-hierarchically

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Learning to Plan Hierarchically from Curriculum

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