https://github.com/yobibyte/unitary-scalarization-dmtl
공식 코드In Defense of the Unitary Scalarization for Deep Multi-Task Learning
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/yohannnchen/afst
공식 코드https://github.com/yokian/distspectrl
공식 코드DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems
https://github.com/yongchao98/r1-code-interpreter
공식 코드R1-Code-Interpreter: Training LLMs to Reason with Code via Supervised and Reinforcement Learning
https://github.com/yoniLc/Meta-Subspace-Optimization
공식 코드https://github.com/yooceii/Rotation-Translation-and-Cropping-for-Zero-Shot-Generalization
공식 코드Rotation, Translation, and Cropping for Zero-Shot Generalization
https://github.com/younggyoseo/CaDM
공식 코드Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
https://github.com/younggyoseo/apv
공식 코드Reinforcement Learning with Action-Free Pre-Training from Videos
https://github.com/younggyoseo/trajectory_mcl
공식 코드Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
https://github.com/youpengzhao/crashed_agent
공식 코드Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
https://github.com/youseob/pin
공식 코드Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
https://github.com/yqpqry/sqog
공식 코드Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood
https://github.com/ysaikai/rlir
공식 코드Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback
https://github.com/ysmei97/mac-po
공식 코드MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
https://github.com/yu-utah/respect
공식 코드RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs
https://github.com/yuan-12138/vesnet-rl
공식 코드VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation
https://github.com/yuanchenbei/awesome-graphs-meet-agents
공식 코드Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
https://github.com/yuanhangzhang98/ml_quantum_compiling
공식 코드https://github.com/yuanmingqi/adept
공식 코드https://github.com/yuanying-cc/td3-rkl
공식 코드TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets
https://github.com/yuchen413/text2image_safety
공식 코드https://github.com/yudasong/PCMLP
공식 코드PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
https://github.com/yudasong/briee
공식 코드Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
https://github.com/yueeeeeeee/hrpo
공식 코드https://github.com/yuelin301/informationdesignmarl
공식 코드https://github.com/yueyang130/odpr
공식 코드https://github.com/yuezhu95/intelligent-traffic-light-via-reinforcement-learning
공식 코드Intelligent Traffic Light via Policy-based Deep Reinforcement Learning
https://github.com/yufeiwang63/ROLL
공식 코드ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
https://github.com/yufengyuan/ur5_async_rl
공식 코드Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots
https://github.com/yuguangyue/CARSM
공식 코드https://github.com/yuki-kadokawa/cyclic-policy-distillation
공식 코드https://github.com/yuleiqin/raif
공식 코드Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models
https://github.com/yunfeixie233/vigal
공식 코드https://github.com/yunglau/QGFN
공식 코드https://github.com/yunhui1998/gymjsp
공식 코드https://github.com/yunqiuxu/h-kga
공식 코드Generalization in Text-based Games via Hierarchical Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.