Decision Making
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 2
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/llm-transparency-tool
LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Models
https://github.com/facebookresearch/minihack
Generalization to New Sequential Decision Making Tasks with In-Context Learning
https://github.com/facebookresearch/rl/blob/main/examples/sac/sac.py
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/fair-preprocessing/nips2017
https://github.com/fdcl-gwu/gym-rotor
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/federico-pizarrobejarano/safe-control-gym
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
https://github.com/ferryjul/fairCORELS
https://github.com/filippogiruzzi/semantic_segmentation
https://github.com/fiorenza2/OffCon3
https://github.com/flow-project/ray
https://github.com/flowersteam/rl_stats
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/fortuinlab/la-nam
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/foundation-model-research/DiffLens
Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability
https://github.com/francidellungo/Minigrid_HCI-project
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
https://github.com/frgfm/torch-cam
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
https://github.com/galdeia/feat
https://github.com/gammatauai/leetcode-hard-gym
Reflexion: Language Agents with Verbal Reinforcement Learning
https://github.com/geekJZY/arcticnet
https://github.com/geods/region2vec-gat
https://github.com/gijskoning/ReproducingCURL
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/gitforreview/dsaa_journal_review
https://github.com/gitlimlab/Relation-Network-Tensorflow
https://github.com/glambrechts/informed-dreamer
https://github.com/google-deepmind/graphcast
GraphCast: Learning skillful medium-range global weather forecasting
https://github.com/google-deepmind/streetlearn
https://github.com/google-research/google-research/tree/master/neural_additive_models
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/google-research/google-research/tree/master/protoattend
https://github.com/google/neural-logic-machines
https://github.com/googleinterns/controllabledl
https://github.com/grayswanai/nanogcg
https://github.com/greerviau/GlorifiedCruiseControl
https://github.com/gsq7474741/Paddle-NCR
https://github.com/guanton/umfi_python
Information-Theoretic State Variable Selection for Reinforcement Learning
https://github.com/gwthomas/gtml
https://github.com/h-aboutalebi/SparceReward
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.