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/MOVzeroOne/DQN
https://github.com/MSRDL/Deep4Cast
https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/MVerghese/NN-Dynamics-Sparse-Reward
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/MYPETFISH/PGPortfolio
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/MagaliDrumare/Augmented-Random-Search-ARS-Intuition
Simple random search provides a competitive approach to reinforcement learning
https://github.com/Mahoumaru/t-momentum
https://github.com/MaksimIM/aml
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
https://github.com/Maltimore/robotic_nanofabrication
Autonomous robotic nanofabrication with reinforcement learning
https://github.com/Manchery/iql-pytorch
https://github.com/ManooshSamiei/Object-Detection-Deep-Reinforcement-Learning
https://github.com/MarcAntoineAlex/darts
https://github.com/MarsEleven/car_racer_RL
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/MateuszJanda/netris-ai-robot
https://github.com/MathPhysSim/PER-NAF
https://github.com/MatheusMRFM/A3C-LSTM-with-Tensorflow
https://github.com/Mathieu-Seurin/impact-driven-exploration
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
https://github.com/MattJud/gnn_jssp
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
https://github.com/MatthieuSarkis/Portfolio-Optimization-and-Goal-Based-Investment-with-Reinforcement-Learning
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/MaximeBouton/AutomotiveSafeRL
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Deep_Reinforcement_Learning_with_Double_Q_learning
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Playing_Atari_with_Deep_Reinforcement_Learning
https://github.com/Medabid1/Bayes-by-backprob
https://github.com/Medabid1/RL_Project
https://github.com/MehdiChelh/Zoo
https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints
https://github.com/MehranTaghian/DQN-Trading
Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning
https://github.com/MerceaOtniel/HybridAlpha
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/Mind23-2/MindCode-101/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-107
https://github.com/Mind23-2/MindCode-163
https://github.com/Mind23-2/MindCode-3/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-5/tree/main/pnasnet
https://github.com/Mind23-2/MindCode-55
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.