https://github.com/ku2482/wappo.pytorch
Visual Transfer for Reinforcement Learning via Wasserstein Domain Confusion
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/kubeflow/katib
https://github.com/kumaa001/Tractable_RL_for_STL_Objectives
Tractable Reinforcement Learning of Signal Temporal Logic Objectives
https://github.com/kumar-shridhar/BayesianConvNet
https://github.com/kumar-shridhar/PyTorch-BayesianCNN
https://github.com/kunalp117/youtube-commenting-analysis
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior
https://github.com/kushagra06/DDPG
https://github.com/kushagra06/SAC
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ky941122/darts-experiment
https://github.com/l1t1/lz19
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/la3lma/Chez
Dueling Network Architectures for Deep Reinforcement Learning
https://github.com/la3lma/chezjulia
Dueling Network Architectures for Deep Reinforcement Learning
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lachisis/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/laiviet/maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/lambertinialessandro/RL-FinalProject
What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes
https://github.com/langorou/vamperouge
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/lanqingli1993/focal-iclr
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/lasseuth1/blood_bowl2
Automated Curriculum Learning by Rewarding Temporally Rare Events
https://github.com/lava-lab/safe-slac
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
https://github.com/lbenbaccar/Deep-Reinforcement-Learning-with-Double-Q-Learning
https://github.com/lduml/blog
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/leandro-sbetancourt/gym-mm-pooled-competition
https://github.com/learn-to-race/l2r
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/learnermaxRL/PPO_A2C
https://github.com/leehe228/LogisticsEnv
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
https://github.com/leehoon7/flow
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/leela-zero/leela-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/leelachesszero/lczero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/leggedrobotics/legged_gym
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
https://github.com/leggedrobotics/rsl_rl
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
https://github.com/lemon234071/oc_parlai
https://github.com/leo27945875/ASAF-1
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
https://github.com/leo27945875/Adversarial-Soft-Advantage-Fitting-1
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
https://github.com/leoandeol/cods
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
https://github.com/leopard-ai/betty
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.