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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/geraudnt/boolean_composition
https://github.com/geshang777/FOCUS
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
https://github.com/geyang/gym-distracting-control
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels
https://github.com/geyang/plan2vec
Plan2Vec: Unsupervised Representation Learning by Latent Plans
https://github.com/gg-Dema/VBLRL
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
https://github.com/ghliu/pytorch-ddpg
https://github.com/ghostFaceKillah/expert
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
https://github.com/gijskoning/ReproducingCURL
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/gingkg/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/gingkg/smac
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/giorgiopiatti/suber
SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems
https://github.com/github-jnauta/pytorch-pne
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/gkassel/pyaixi
https://github.com/glambrechts/informed-dreamer
https://github.com/glassices/sokoban_planner
Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning
https://github.com/glinscott/leela-chess
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/gmh14/IRLAS
IRLAS: Inverse Reinforcement Learning for Architecture Search
https://github.com/gnns4hri/socnavgym
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
https://github.com/goldbattle/snakes_mal
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/gomerudo/nas-dmrl
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/gomerudo/openai-baselines
https://github.com/goodbyeearth/myVBC
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
https://github.com/goodls-cs/leela-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/google-deepmind/acme
Acme: A Research Framework for Distributed Reinforcement Learning
https://github.com/google-deepmind/android_env
https://github.com/google-deepmind/open_spiel
https://github.com/google-deepmind/pysc2
https://github.com/google-deepmind/scalable_agent
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
https://github.com/google-deepmind/spriteworld
https://github.com/google-deepmind/streetlearn
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
https://github.com/google-research/android_world
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
https://github.com/google-research/dads
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
https://github.com/google-research/dice_rl
https://github.com/google-research/dreamer
https://github.com/google-research/google-research/tree/master/bisimulation_aaai2020
Dopamine: A Research Framework for Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.