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/yochaiz/darts-UNIQ
https://github.com/yogi2578/GameEngineLearning
Action-Conditional Video Prediction using Deep Networks in Atari Games
https://github.com/yohkaz/BasicRayTracer
https://github.com/yongbowin/san_mrc_annotation
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/yoojungsun0/Psych239
https://github.com/yooli23/ParlAI
https://github.com/yoomambo/BayesianOptimization_Tuned
https://github.com/yoonholee/MT-net
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/york2210/MedicalChatbot-HRL
Hierarchical Reinforcement Learning for Automatic Disease Diagnosis
https://github.com/yosider/merlin
https://github.com/young-geng/cql
https://github.com/young-geng/jaxcql
https://github.com/younggyoseo/RE3
State Entropy Maximization with Random Encoders for Efficient Exploration
https://github.com/younggyoseo/mwm
Reinforcement Learning with Action-Free Pre-Training from Videos
https://github.com/yredwood/fewshot_blogpost
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/yrlu/irl-imitation
https://github.com/yschroecker/universal_value_density_estimation
https://github.com/ythuangyt/Robust-Reinforcement-Learning-via-Adversarial-training-with-Langevin-Dynamics
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
https://github.com/yuanfeisiyuetian/seqgan-modbusTCP
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/yuant95/SMARTS_VCR
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/yueqiw/gqn-world-model
https://github.com/yufeiwang63/L2D-learning-to-discretize
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning
https://github.com/yufeiwang63/rl-vlm-f
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
https://github.com/yuguangyue/Implicit_Distributional_RL
https://github.com/yuke93/RL-Restore
Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning
https://github.com/yukezhu/tensorflow-reinforce
https://github.com/yun-kwak/decision-transformer-jax
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/yusme/DDPG
https://github.com/yusukeurakami/dreamer-pytorch
https://github.com/yusukeurakami/plan2explore-pytorch
https://github.com/yusukeurakami/slac-playground
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
https://github.com/yuta0821/agent57_pytorch
https://github.com/yuxiang-lai117/med-r1
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models
https://github.com/ywchao/merel-mocap-gail
Learning human behaviors from motion capture by adversarial imitation
https://github.com/yytyvonne/DQN_agent_Chatbot
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.