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/ArayCHN/HAAR-A-Hierarchical-RL-Algorithm
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards
https://github.com/Archibald-Lafraik/pipps
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
https://github.com/ArchieGertsman/spark-sched-sim
https://github.com/ArnaudFickinger/adversarial-surprise
https://github.com/ArnoutDevos/maml-cifar-fs
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Arrabonae/openai_DDDQN
https://github.com/Asap7772/OfflineRlWorkflow
A Workflow for Offline Model-Free Robotic Reinforcement Learning
https://github.com/Ashish-Tripathy/AI_SelfDriving
Model-free Deep Reinforcement Learning for Urban Autonomous Driving
https://github.com/AshrithSagar/option-critic
https://github.com/AstraZeneca/awesome-shapley-value
https://github.com/AtmaHou/UserSimulator
https://github.com/AurelianTactics/bcq_tensorflow
https://github.com/AurelianTactics/dqfd-with-keras
https://github.com/AurelianTactics/dqnclipped_dqnreg_prelim_implementation
https://github.com/AutumnWu/Streamlined-Off-Policy-Learning
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/AvivNavon/AuxiLearn
https://github.com/AyeshaAmjad0828/Unstructured-Data-Extraction
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents
https://github.com/B-C-WANG/ReinforcementLearningInAutoPilot
https://github.com/BBDrive/Meta-SGD-RL
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/BBDrive/TESP
Meta Reinforcement Learning with Task Embedding and Shared Policy
https://github.com/BCHoagland/DARLA
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
https://github.com/BCHoagland/VINS
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling
https://github.com/BFAnas/BranchingDQN_keras
Action Branching Architectures for Deep Reinforcement Learning
https://github.com/BH4/Deep-Reinforcement-Learning
https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/AMPAgent.py
AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control
https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/COPGAgent.py
Clipped-Objective Policy Gradients for Pessimistic Policy Optimization
https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/TD3Agent.py
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/BPYap/Question-Generation
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
https://github.com/BPrasad123/Data_Science_Learning
APES: a Python toolbox for simulating reinforcement learning environments
https://github.com/BY571/CQL
https://github.com/BY571/DQN-Atari-Agents
Dueling Network Architectures for Deep Reinforcement Learning
https://github.com/BY571/FQF-and-Extensions
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
https://github.com/BY571/IQN
Implicit Quantile Networks for Distributional Reinforcement Learning
https://github.com/BY571/Implicit-Q-Learning
https://github.com/BY571/MBPO
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.