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Reinforcement Learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MindSpore-scientific/code-10/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific/code-12/tree/main/HTM-mindspore

HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal Memory

https://github.com/MindSpore-scientific/code-13/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific/code-2/tree/main/translating-math-formula-images

Translating Math Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level Training

https://github.com/MindSpore-scientific/code-8/tree/main/Simba

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

https://github.com/MingzhenY/Projects

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/Minhchuyentoancbn/Continual-Learning

Uncertainty-based Continual Learning with Adaptive Regularization

https://github.com/Mircea-Marian/attract_grid_data_flow_optimization

Free energy-based reinforcement learning using a quantum processor

https://github.com/MishaLaskin/rad

Reinforcement Learning with Augmented Data

https://github.com/MiuGod0126/Bayes_backprop_paddle

Weight Uncertainty in Neural Networks

https://github.com/MiuGod0126/RAM-Paddle

Recurrent Models of Visual Attention

https://github.com/MnMaxon/VR-Gesture-Detection

Unity: A General Platform for Intelligent Agents

https://github.com/MoMe36/BranchingDQN

Action Branching Architectures for Deep Reinforcement Learning

https://github.com/MohammadAsadolahi/DDQN_Deep-Double_Q-Learning-for-solving-OpenAi-Gym-LunarLander-v2-in-python

Deep Reinforcement Learning with Double Q-learning

https://github.com/MohammadAsadolahi/Dueling_Deep-Double_Q-Learning-for-solving-OpenAi-Gym-LunarLander-v2-in-python

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/MohammadAsadolahi/Gradient-Free-Reinforcement-Learning-solving-Openai-gym-LunarLanderV2-by-Evolution-Strategy-python/blob/main/README.md

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/Montherapy/Deep-reinforcement-learning-for-multi-class-imbalanced-classification

Deep Reinforcement Learning with Double Q-learning

https://github.com/MoritzTaylor/maml-rl-tf2

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/MotorCityCobra/C_plusplus_mpo

Maximum a Posteriori Policy Optimisation

https://github.com/MrDaubinet/collaboration-and-competition

Prioritized Experience Replay

https://github.com/MrSyee/pg-is-all-you-need

Continuous control with deep reinforcement learning

https://github.com/MuditShivendra/Traffic_Light_Control

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/Mukundaram/Mukund

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/Multi-Agent-Research-Group/SQDDPG

Shapley Q-value: A Local Reward Approach to Solve Global Reward Games

https://github.com/Murali81/Grad-CAM

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/N0r9st/a2c-jax

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/NACLab/robust-active-inference

Proximal Policy Optimization Algorithms

https://github.com/NOHYC/autonomous_driving_car_project

Continuous control with deep reinforcement learning

https://github.com/Narsil/rl-baselines

Proximal Policy Optimization Algorithms

https://github.com/Nasdin/ReinforcementLearning-AtariGame

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/NeekhilD/Learning-Competition

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

https://github.com/Neo-The1/ThinkingTicTacToe

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/Neoanarika/CNN-using-Swish

Searching for Activation Functions

https://github.com/Neoanarika/Searching-for-activation-functions

Searching for Activation Functions

https://github.com/NervanaSystems/coach

Playing Atari with Deep Reinforcement Learning

https://github.com/NervanaSystems/distiller

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

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