https://github.com/andris955/diplomaterv
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
Papers with Code 아카이브
Deep Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/angelolovatto/raylab
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
https://github.com/anh-nn01/Lunar-Lander-Double-Deep-Q-Networks
https://github.com/anita-hu/TF2-RL
https://github.com/ankur-rc/autodrive_ddpg
https://github.com/ankurhanda/rand_conv
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
https://github.com/anonymous-iclr22/trust-region-in-multi-agent-reinforcement-learning
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/apourchot/ERL
https://github.com/apourchot/ERL-pytorch
https://github.com/apourchot/EvoLearn
https://github.com/araffin/learning-to-drive-in-5-minutes
https://github.com/araffin/sbx
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/aravind0706/neural-mpc
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/aravindsrinivas/neural-mpc
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/aredier/monte_carlo_dropout
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/arneschmidt/bayesian_deep_learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/arnomoonens/yarll
https://github.com/arodriguezca/uncertainty-ts-forecasting
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/arryyao/flow-project-flow
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/arunajit/drl
Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning
https://github.com/asarav/MIT-Deep-Traffic-Solution
https://github.com/asharakeh/probdet
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/ashtawy/deeptraffic
https://github.com/asjad99/Reinforcement-Learning
https://github.com/aspk/Tensorflow_Deep_RL
https://github.com/astanway/gated-conv-nets
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
https://github.com/astooke/accel_rl
https://github.com/astooke/rlpyt/tree/master/rlpyt/ul
Decoupling Representation Learning from Reinforcement Learning
https://github.com/atavakol/action-hypergraph-networks
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/aurelio-amerio/concretedropout
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/austinsilveria/Banana-Collection-DQN
https://github.com/autumnwu/aggressive-q-learning-with-ensembles
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/avalon-benchmark/avalon
Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds
https://github.com/avillemin/Minecraft-AI
https://github.com/avivelor/UnityMachineLearningForProjectButterfly
https://github.com/avivnavon/nash-mtl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.