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Decision Making

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/tmjeong1103/RL_with_RAY

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tobias-opsahl/hybrid-concept-based-models

Promises and Pitfalls of Black-Box Concept Learning Models

https://github.com/tolgaok/offlinerlviaio

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

https://github.com/tomyvazquez/doraa-uy

Optimal Power Flow Using Graph Neural Networks

https://github.com/toni-sm/skrl

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/toshikwa/discor.pytorch

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/toshikwa/soft-actor-critic.pytorch

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/toytiny/radarocc

milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing

https://github.com/trackmania-rl/tmrl

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tristandeleu/gfn-maxent-rl

Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions

https://github.com/tsingqaq/trieste

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

https://github.com/tutujingyugang1/ChatVLA_public

ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge

https://github.com/txyugood/tabnet

TabNet: Attentive Interpretable Tabular Learning

https://github.com/uber-research/D3G

Estimating Q(s,s') with Deep Deterministic Dynamics Gradients

https://github.com/uber-research/ga-world-models

Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/uber-research/go-explore

Go-Explore: a New Approach for Hard-Exploration Problems

https://github.com/ucabcg3/msc_bias_llm_project

Measuring Implicit Bias in Explicitly Unbiased Large Language Models

https://github.com/umersheikh846/WQMIX

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/understanding-ml/COLA

Distributional Counterfactual Explanations With Optimal Transport

https://github.com/uptrain-ai/uptrain

Constitutional AI: Harmlessness from AI Feedback

https://github.com/uribyul/py_ai_clinician

Deep Reinforcement Learning for Sepsis Treatment

https://github.com/ustc-arg/safe-robot-learning-competition

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

https://github.com/ut-austin-rpl/prelude

Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments

https://github.com/vanderschaarlab/prompt-oirl

Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and Beyond

https://github.com/vik-goel/MOREL

Unsupervised Video Object Segmentation for Deep Reinforcement Learning

https://github.com/vimalabs/VIMA

VIMA: General Robot Manipulation with Multimodal Prompts

https://github.com/viniciusguigo/complete_col

Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time

https://github.com/vinid/negotiationarena

Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation

https://github.com/vitaly-emelianov/multistage_fairness

The Price of Local Fairness in Multistage Selection

https://github.com/voot-t/ril_co

Robust Imitation Learning from Noisy Demonstrations

https://github.com/vortezwohl/Autono

ReAct: Synergizing Reasoning and Acting in Language Models

https://github.com/wang-chen/SubT

Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

https://github.com/wang-chen/interestingness_ros

Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

https://github.com/wangxiao5791509/Tracking-with-Deep-Reinforcement-Learning

Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search

https://github.com/wangyongjie-ntu/CFAI

Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

https://github.com/wangyongjie-ntu/Counterfactual-Explanations-Pytorch

Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.