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Q-Learning

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The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>

코드

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

https://github.com/MuhangTian/Fair-MORL-AAMAS

공식 코드

Welfare and Fairness in Multi-objective Reinforcement Learning

https://github.com/NUS-LID/RENAULT

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Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning

https://github.com/NVIDIA/GraphQSat

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Can $Q$-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

https://github.com/NeuroCSUT/DeepMind-Atari-Deep-Q-Learner-2Player

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Multiagent Cooperation and Competition with Deep Reinforcement Learning

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

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Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration

https://github.com/QDPP-GitHub/QDPP

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Multi-Agent Determinantal Q-Learning

https://github.com/RLforlife/VER

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Revisiting Prioritized Experience Replay: A Value Perspective

https://github.com/Rich-King395/Path-Planning-based-on-Reinforcement-Learning

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Research on Robot Path Planning Based on Reinforcement Learning

https://github.com/SamuelMallick/dmpcrl-concept

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Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator

https://github.com/ShangtongZhang/DeepRL

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Breaking the Deadly Triad with a Target Network

https://github.com/Shilun-Allan-Li/2048-reinforcement_learning

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Playing 2048 With Reinforcement Learning

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

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Dropout Q-Functions for Doubly Efficient Reinforcement Learning

https://github.com/ULieu/RL_3well

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Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning

https://github.com/WonderSeven/DeepAugNet

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Automatic Data Augmentation by Learning the Deterministic Policy

https://github.com/Yubin-Liu/Hybrid-Q-Learning-Network-Approach-for-MPLP/blob/main/README.md

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Route Planning for Last-Mile Deliveries Using Mobile Parcel Lockers: A Hybrid Q-Learning Network Approach

https://github.com/ZehaoJin/Reinforcement-Learning-2019

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Learning Principle of Least Action with Reinforcement Learning

https://github.com/ZhengyaoJiang/graphbackup

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Graph Backup: Data Efficient Backup Exploiting Markovian Transitions

https://github.com/ZiyuanMa/DHC

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Distributed Heuristic Multi-Agent Path Finding with Communication

https://github.com/aVariengien/self-organized-control

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Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent

https://github.com/aalmuzairee/dmcgb2

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A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning

https://github.com/ago109/active-neural-generative-coding

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Backprop-Free Reinforcement Learning with Active Neural Generative Coding

https://github.com/ai4co/eph-mapf

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Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding

https://github.com/alevine0/reengage

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Goal-Conditioned Q-Learning as Knowledge Distillation

https://github.com/alexgaarciia/packetopticallatencyrl

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Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry

https://github.com/alexpiche/fr-tmlr

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Bridging the Gap Between Target Networks and Functional Regularization

https://github.com/alibeikmohammadi/compfedrl

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Compressed Federated Reinforcement Learning with a Generative Model

https://github.com/alibeikmohammadi/opennars-for-applications

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NARS vs. Reinforcement learning: ONA vs. Q-Learning

https://github.com/alirezashamsoshoara/inverse-rl-apprenticeship-learning-uav-communication

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Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning

https://github.com/amartyamukherjee/ReinforcementLearningCartPosition

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A Comparison of Reward Functions in Q-Learning Applied to a Cart Position Problem

https://github.com/amazon-research/meta-q-learning

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Meta-Q-Learning

https://github.com/amirhosseinzlf/starla

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A Search-Based Testing Approach for Deep Reinforcement Learning Agents

https://github.com/andrei-t-neagu/drl_in_finance

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Deep Reinforcement Learning Algorithms for Option Hedging

https://github.com/andyzeng/visual-pushing-grasping

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Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

https://github.com/anindyasarkarIITH/Reward_Delay_Attack_DRL

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Reward Delay Attacks on Deep Reinforcement Learning

https://github.com/ann-w/solving_drl_tasks_with_es_and_linear_policy_networks

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Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks

https://github.com/anonicml2019/icml_2019_state_abstraction

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Learning State Abstractions for Transfer in Continuous Control

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