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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/MOCR/DDPG

공식 코드

Actor-critic versus direct policy search: a comparison based on sample complexity

https://github.com/MadryLab/implementation-matters

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Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

https://github.com/Mahmoud-Selim/Safe-Reinforcement-Learning-for-Black-Box-Systems-Using-Reachability-Analysis

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Safe Reinforcement Learning Using Black-Box Reachability Analysis

https://github.com/Mahoumaru/TAdam

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TAdam: A Robust Stochastic Gradient Optimizer

https://github.com/MajidMoghadam2006/RL-frenet-trajectory-planning-in-CARLA

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An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

https://github.com/MajidMoghadam2006/deepcars-reinforcement-learning

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A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning

https://github.com/Maluuba/jumping-task

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Learning Invariances for Policy Generalization

https://github.com/ManipulativeConsultant/AutonomousDrivingCookbook

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Adaptive Symmetric Reward Noising for Reinforcement Learning

https://github.com/ManuelFritsche/vpc

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Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning

https://github.com/MarcoMeter/neroRL

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Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning

https://github.com/MarcusOlivecrona/REINVENT

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Molecular De Novo Design through Deep Reinforcement Learning

https://github.com/MarkCodering/Deep-Reinforcement-Learning-using-quDNN

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Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems

https://github.com/MarkusRabe/cadet

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Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning

https://github.com/MartinBertran/InstanceAgnosticPolicyEnsembles

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Instance based Generalization in Reinforcement Learning

https://github.com/MarvinLer/pypownet

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Design and implementation of an environment for Learning to Run a Power Network (L2RPN)

https://github.com/MatNLP/KEHRL

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KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning

https://github.com/MathPhysSim/FERMI_RL_Paper

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Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL

https://github.com/MatheusMRFM/STIM

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Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning

https://github.com/MatiasAlvo/Neural_inventory_control

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Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization

https://github.com/McGill-NLP/polytropon

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Combining Modular Skills in Multitask Learning

https://github.com/MehdiKhamassi/SocialMetaLearning

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Active exploration in parameterized reinforcement learning

https://github.com/Mehooz/BIRD_code

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Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

https://github.com/MeixinZhu/Velocity_control

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Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving

https://github.com/MichaelAllen1966/learninghospital

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Integrating Deep Reinforcement Learning Networks with Health System Simulations

https://github.com/MichaelAllen1966/qambo

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Developing an OpenAI Gym-compatible framework and simulation environment for testing Deep Reinforcement Learning agents solving the Ambulance Location Problem

https://github.com/Miffyli/asv-cm-reinforce

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An initial investigation on optimizing tandem speaker verification and countermeasure systems using reinforcement learning

https://github.com/Miffyli/rl-action-space-shaping

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Action Space Shaping in Deep Reinforcement Learning

https://github.com/Miffyli/rl-human-prior-tricks

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Distilling Reinforcement Learning Tricks for Video Games

https://github.com/MiguelAguilera/Adaptation-to-criticality-through-organizational-invariance

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Adaptation to criticality through organizational invariance in embodied agents

https://github.com/MiguelAguilera/Criticality-as-It-Could-Be

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Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents

https://github.com/MikeTimmerman-ae/RL_Drone_Control

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Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control

https://github.com/MikulasZelinka/pyfiction

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Using reinforcement learning to learn how to play text-based games

https://github.com/MishaLaskin/curl

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CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/MiuLab/DDQ

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Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

https://github.com/MiuLab/KB-InfoBot

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Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

https://github.com/MiuLab/MUSE

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MUSE: Modularizing Unsupervised Sense Embeddings

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.