https://github.com/maoyixiu/dmg
공식 코드Doubly Mild Generalization for Offline Reinforcement 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/marc-rigter/1r2r
공식 코드https://github.com/marc-rigter/polygrad-world-models
공식 코드https://github.com/marc-rigter/rambo
공식 코드RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
https://github.com/marcelbinz/resource-rational-reinforcement-learning
공식 코드Modeling Human Exploration Through Resource-Rational Reinforcement Learning
https://github.com/marcovona99/four-roll-mill-rl
공식 코드Stabilising viscous extensional flows using Reinforcement Learning
https://github.com/marcschlichting/lstmspatialencoding
공식 코드Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning
https://github.com/marcuxu/psmg
공식 코드PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
https://github.com/marekpetrik/craam2
공식 코드Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
https://github.com/marina-sp/reinform
공식 코드ReInform: Selecting paths with reinforcement learning for contextualized link prediction
https://github.com/mario-holubar/RacingRL
공식 코드Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPO
https://github.com/mariuslindegaard/curiosity_baselines
공식 코드Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement Learning
https://github.com/marko-ruman/RL-Correspondence-Learner
공식 코드https://github.com/marko-vasic/moet
공식 코드MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
https://github.com/markpwoodward/active_osl
공식 코드https://github.com/marlbenchmark/on-policy
공식 코드The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/marmotlab/fcmnet
공식 코드FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems
https://github.com/martin592/hybrid_AuGMEnT
공식 코드Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory
https://github.com/martinballa/pytag
공식 코드PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games
https://github.com/martius-lab/SMORL
공식 코드Self-supervised Visual Reinforcement Learning with Object-centric Representations
https://github.com/martius-lab/cid-in-rl
공식 코드Causal Influence Detection for Improving Efficiency in Reinforcement Learning
https://github.com/martius-lab/depRL
공식 코드DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems
https://github.com/martius-lab/hits
공식 코드https://github.com/martius-lab/iCEM
공식 코드Sample-efficient Cross-Entropy Method for Real-time Planning
https://github.com/martkartasev/btbackchainingrl
공식 코드Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning
https://github.com/marwaltz/tud_rl
공식 코드Addressing Maximization Bias in Reinforcement Learning with Two-Sample Testing
https://github.com/marwaltz/two-step-doa
공식 코드https://github.com/mary-wu/smdp
공식 코드https://github.com/maryam-abdool/swim-control
공식 코드https://github.com/masa-ue/RLfinetuning_Diffusion_Bioseq
공식 코드Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review
https://github.com/masud99r/bae
공식 코드Bootstrap Advantage Estimation for Policy Optimization in Reinforcement Learning
https://github.com/masud99r/thinker
공식 코드https://github.com/mathieurita/llm_demonstration_guided_rl
공식 코드Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning
https://github.com/mathieutuli/ltl-gata
공식 코드https://github.com/matibilkis/marek
공식 코드Real-time calibration of coherent-state receivers: learning by trial and error
https://github.com/matrl-project/matrl
공식 코드A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.