https://github.com/haoranwang-tj/gcmr_aclg_official
공식 코드Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout
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/haosulab/maniskill2
공식 코드ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills
https://github.com/haozheji/exact-optimization
공식 코드Towards Efficient Exact Optimization of Language Model Alignment
https://github.com/haozhetian/rl-ar
공식 코드Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems
https://github.com/haozhiqi/hora
공식 코드https://github.com/happy-yan/DACER-Diffusion-with-Online-RL
공식 코드https://github.com/hardmaru/astool
공식 코드https://github.com/harfang3d/dogfight-sandbox-hg2
공식 코드https://github.com/hari-sikchi/DVL
공식 코드Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
https://github.com/hari-sikchi/LBPO
공식 코드https://github.com/harrylui98/zoac-tevc
공식 코드Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems
https://github.com/harsh6gpt1/adaptivetwotimeRL
공식 코드https://github.com/haruhi07/pbrl-tls
공식 코드Towards Abstractive Timeline Summarisation using Preference-based Reinforcement Learning
https://github.com/harvard-cns/teal
공식 코드Teal: Learning-Accelerated Optimization of WAN Traffic Engineering
https://github.com/harvard-edge/airlearning
공식 코드Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation
https://github.com/harvard-edge/quarl
공식 코드QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
https://github.com/hassyGo/NLG-RL
공식 코드Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction
https://github.com/hbayerlein/uav_data_harvesting
공식 코드UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
https://github.com/hca-neurips2019/hca
공식 코드https://github.com/hcdm/xrec
공식 코드Rethinking Conversational Recommendations: Is Decision Tree All You Need?
https://github.com/hcmlab/ganterfactual-rl
공식 코드https://github.com/heasung-kim/safe-rl-deployment-for-5g
공식 코드https://github.com/heidelkin/BIPNMT
공식 코드A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation
https://github.com/heimy2000/guandan
공식 코드https://github.com/heiner/scalable_agent
공식 코드https://github.com/heitzjon/mt-join-query-optimization-with-drl
공식 코드Join Query Optimization with Deep Reinforcement Learning Algorithms
https://github.com/hellochick/FICM
공식 코드https://github.com/helmholtzai-fzj/hearts-gym
공식 코드https://github.com/hemerson1/offline-glucose
공식 코드Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes
https://github.com/hendrycks/ethics
공식 코드https://github.com/hengruicai/djl
공식 코드Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
https://github.com/henrycharlesworth/PlanGAN
공식 코드PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals
https://github.com/henrycharlesworth/SCDM
공식 코드https://github.com/henrycharlesworth/big2_PPOalgorithm
공식 코드Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information
https://github.com/henrycharlesworth/dexterous-gym
공식 코드https://github.com/herolab-uga/bsac
공식 코드Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.