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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/tkarr21/multiagent-particle-envs

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/tkcoding/DeepRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/tkcoding/Stock_DRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/tliu1997/rnac

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tlohr/nfsu2-ai

Playing Atari with Deep Reinforcement Learning

https://github.com/tmjeong1103/RL_with_RAY

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tomaszsmaruj25/Twin-Delayed-DDPG-Implementation

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/toni-sm/skrl

Playing Atari with Deep Reinforcement Learning

https://github.com/toshikwa/discor.pytorch

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/toshikwa/soft-actor-critic.pytorch

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/trackmania-rl/tmrl

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/typoverflow/OfflineRL-Lib

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/uber-common/deep-neuroevolution

Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

https://github.com/ugo-nama-kun/DQN-chainer

Playing Atari with Deep Reinforcement Learning

https://github.com/umersheikh846/WQMIX

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/uos-plccn/gym-pbn

Deep Reinforcement Learning for Control of Probabilistic Boolean Networks

https://github.com/ustcmike/adaeq_neurips21

Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

https://github.com/utarumo/RL_implementation

Deep Reinforcement Learning with Double Q-learning

https://github.com/vincentpalma/DQN-for-CaRL

Playing Atari with Deep Reinforcement Learning

https://github.com/vsquareg/RL_ERA

Playing Atari with Deep Reinforcement Learning

https://github.com/vu-resilient-distributed-systems/lectures-fall-2019

Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems

https://github.com/wangbx66/differentially-private-q-learning

Privacy-preserving Q-Learning with Functional Noise in Continuous State Spaces

https://github.com/wassname/rl-portfolio-management

Continuous control with deep reinforcement learning

https://github.com/watchernyu/spinningup-drl-prototyping

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/water-bridge/2020rl

Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima

https://github.com/wmol4/Pytorch_DDQN_Unity_Navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/wpiszlogin/driver_critic

Continuous control with deep reinforcement learning

https://github.com/wsjeon/multiagent-particle-envs-maac

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/wsjeon/multiagent-particle-envs-v2

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/wtingda/DeepRLBreakout

Deep Reinforcement Learning with Double Q-learning

https://github.com/wuxx1016/Reinforcement-Learning-in-Finance

QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds

https://github.com/wwydmanski/rl_tennis

Continuous control with deep reinforcement learning

https://github.com/xValentim/Steering_Behaviors_with_pygame

Playing Atari with Deep Reinforcement Learning

https://github.com/xavierchanglingli/regularized-optimal-experience-replay

Extreme Q-Learning: MaxEnt RL without Entropy

https://github.com/xgfelicia/Reinforcement-Learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/xiexiexiaoxiexie/Udacity-self-driving-car-engineer-P7-Highway-Driving

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

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