https://github.com/crashmoon/MAML-Pytorch-Multi-GPUs
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Papers with Code 아카이브
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/crazydonkey200/neural-symbolic-machines
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
https://github.com/crazyleg/TD3-reacher
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/createamind/softlearning
https://github.com/cross32768/Dreamer_PyTorch
https://github.com/crowdAI/marLo
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
https://github.com/cshannonn/blackscholes_nas
https://github.com/csxeba/Reproducing-Q-AGREL
A Biologically Plausible Learning Rule for Deep Learning in the Brain
https://github.com/csyanbin/MAML-Pytorch-Multi-GPUs
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/ctallec/world-models
https://github.com/cts198859/deeprl_dist
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/cuijiaxun/MITC-Project
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/cww97/visual-language-grasping
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
https://github.com/cyanrain7/trpo-in-marl
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/cycraig/gym-goal
https://github.com/cycraig/gym-platform
https://github.com/cyoon1729/Multi-agent-reinforcement-learning
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/cyrusneary/rm-cooperative-marl
Reward Machines for Cooperative Multi-Agent Reinforcement Learning
https://github.com/czen88/qtrader
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
https://github.com/czgdp1807/bayesiandl
https://github.com/czh513/Hierarchical-Critics-Assignment-for-Multi-agent-Reinforcement-Learning
https://github.com/czh513/RL-Hierarchical-Critics
https://github.com/czh513/Reinforcement-Learning-from-Hierarchical-Critics
https://github.com/d-eremeev/ADM-VRP
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
https://github.com/daggertye/GAN-Q-Learning
https://github.com/daje0601/Google_SCoRe
Training Language Models to Self-Correct via Reinforcement Learning
https://github.com/dajiangsuo/EV_mixtedAutonomy
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/damedollaforthree/nrml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/dan-lennox/ml-udacity-quadcopter-rl
https://github.com/dangwalp/qg
Machine Comprehension by Text-to-Text Neural Question Generation
https://github.com/danielkelshaw/ConcreteDropout
https://github.com/danielkelshaw/RadialBNN
https://github.com/danielkelshaw/WeightUncertainty
https://github.com/daniellawson9999/forwards_rl
End-to-End Robotic Reinforcement Learning without Reward Engineering
https://github.com/daniellawson9999/online-decision-transformer
https://github.com/danielpolimac/Ispit_Inteligentni_Agenti
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.