https://github.com/IanYangChina/taros2021codes
공식 코드An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet
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/ImperialNLP/pysimt
공식 코드Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation
https://github.com/Improbable-AI/dw-offline-rl
공식 코드Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
https://github.com/IndustAI/risk-and-uncertainty
공식 코드Estimating Risk and Uncertainty in Deep Reinforcement Learning
https://github.com/IndustAI/visual-domain-randomization
공식 코드Robust Visual Domain Randomization for Reinforcement Learning
https://github.com/Information-Fusion-Lab-Umass/causal_transfer_learning
공식 코드https://github.com/InsaneMonster/pasqualini2019prngrl
공식 코드Pseudo Random Number Generation: a Reinforcement Learning approach
https://github.com/InsaneMonster/pasqualini2020prngrl
공식 코드Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks
https://github.com/InsaneMonster/telerl2021
공식 코드Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic
https://github.com/IntelLabs/DNS
공식 코드DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
https://github.com/Intelligent-Reliable-Autonomous-Systems/WOFOSTGym
공식 코드WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies
https://github.com/Intelligent-Systems-Research-Group/RL4MicrostructureEvolution
공식 코드Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization
https://github.com/IoannisKansizoglou/Deep-Feature-Space
공식 코드https://github.com/IouJenLiu/HTS-RL
공식 코드https://github.com/IouJenLiu/PIC
공식 코드PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
https://github.com/IrisLi17/self-imitation-via-reduction
공식 코드Solving Compositional Reinforcement Learning Problems via Task Reduction
https://github.com/IsmaelTito/SEC
공식 코드Sequential memory improves sample and memory efficiency in Episodic Control
https://github.com/JBLanier/pipeline-psro
공식 코드Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
https://github.com/JF-Hu/Continual_Diffuser
공식 코드https://github.com/JHL-HUST/VSR-LKH
공식 코드https://github.com/JLu2022/DWPI
공식 코드Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning
https://github.com/JacobHA/RL4Dosing
공식 코드Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics
https://github.com/JacobPfau/procgenAISC
공식 코드https://github.com/Jacobkooi/-Un-Controllable_Features
공식 코드https://github.com/Jacobkooi/InclinedDroneLander
공식 코드Inclined Quadrotor Landing using Deep Reinforcement Learning
https://github.com/JaeyoungLee-UoA/PIs-for-RL-Problems-in-CTS
공식 코드https://github.com/JakobStruye/HTMRL
공식 코드HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal Memory
https://github.com/JamesR-J/multi_agent_climate_pathways
공식 코드Crafting desirable climate trajectories with RL explored socio-environmental simulations
https://github.com/JamieMair/rledts
공식 코드A reinforcement learning approach to rare trajectory sampling
https://github.com/JanMatas/Rainbow_ddpg
공식 코드Sim-to-Real Reinforcement Learning for Deformable Object Manipulation
https://github.com/JannerM/mbpo
공식 코드https://github.com/JannerM/spatial-reasoning
공식 코드https://github.com/JannerM/trajectory-transformer
공식 코드Offline Reinforcement Learning as One Big Sequence Modeling Problem
https://github.com/JasonMa2016/CODAC
공식 코드https://github.com/JasonZhujp/RELIEF
공식 코드RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning
https://github.com/JaydenTeoh/MORL-Generalization
공식 코드On Generalization Across Environments In Multi-Objective Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.