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/2023-MindSpore-1/ms-code-211/tree/main/DecoMR
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pnasnet
https://github.com/2023-MindSpore-1/ms-code-218/tree/main/deepbsde
https://github.com/2023-MindSpore-1/ms-code-220/tree/main/pmdb
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
https://github.com/2023-MindSpore-1/ms-code-221/tree/main/qmix
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/2023-MindSpore-1/ms-code-52
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/TCN
Time-Contrastive Networks: Self-Supervised Learning from Video
https://github.com/2023-MindSpore-4/Code-5/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_11_optimization
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/decision_transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/2855239858/CenLight-Traffic-Grid-Signal-Optimization-viaAction-and-State-Decomposition
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/3neutronstar/traffic-optimization_RL
https://github.com/4rChon/NL-FuN
https://github.com/5410tiffany/EIIE-keras
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/62subinh/deeprl_safety_specification
https://github.com/7starsea/Prioritized-Experience-Replay
https://github.com/ABKGroup/PlacementEssentialReadings
https://github.com/ACampero/dopamine
https://github.com/ACampero/hsp
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning
https://github.com/ADGEfficiency/dsr_rl
https://github.com/AI-RG/rl-experiments
https://github.com/AI4Finance-LLC/FinRL
Practical Deep Reinforcement Learning Approach for Stock Trading
https://github.com/AI4Finance-LLC/FinRL-Library
Practical Deep Reinforcement Learning Approach for Stock Trading
https://github.com/AI4Finance-LLC/Liquidation-Analysis-using-Multi-Agent-Reinforcement-Learning-ICML-2019
Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis
https://github.com/AIDefender/MyDiscor
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
https://github.com/AIJoris/DPAC-DialogueGAN
https://github.com/AIRLegend/ChessRL
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/AMR-/Conservative-Q-Improvement
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy
https://github.com/ARISE-Initiative/robomimic
What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
https://github.com/ARISE-Initiative/robosuite-benchmark
https://github.com/AWLyrics/SeqGAN_Poem
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/AashrayAnand/rubiks-cube-reinforcement-learning
https://github.com/Abdelhamid-bouzid/Distributional-RL
https://github.com/Abdelhamid-bouzid/Multi-Agent-Deep-Deterministic-Policy-Gradient
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/Abhaya1998/Self-Driving-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.