https://github.com/lancopku/unpaired-sentiment-translation
공식 코드Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
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/langfengq/timemaster
공식 코드TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning
https://github.com/langfengq/verl-agent
공식 코드https://github.com/lanyavik/BAIL
공식 코드BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
https://github.com/lara-martin/StoryPlot-RewardShaping
공식 코드Controllable Neural Story Plot Generation via Reward Shaping
https://github.com/large-scale-sustainable-computing-lsc/green-code
공식 코드GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation
https://github.com/larocs/offline-rl-suvey
공식 코드A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
https://github.com/lascavana/rl2branch
공식 코드https://github.com/lasgroup/aceirl
공식 코드https://github.com/lasgroup/lbsgd-rl
공식 코드Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
https://github.com/lasgroup/model-based-rl
공식 코드When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
https://github.com/laure-crochepierre/reinforcement-based-grammar-guided-symbolic-regression
공식 코드https://github.com/lava-lab/atm
공식 코드Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring
https://github.com/lava-lab/cool-mc
공식 코드Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking
https://github.com/lava-lab/mc_pia
공식 코드Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking
https://github.com/layer6ai-labs/bnpo
공식 코드https://github.com/lcmou/DRL4BS
공식 코드Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification
https://github.com/lcpo-rl/lcpo
공식 코드Online Reinforcement Learning in Non-Stationary Context-Driven Environments
https://github.com/lcswillems/torch-ac
공식 코드Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-Interaction
https://github.com/ld-ing/qdhf
공식 코드Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization
https://github.com/ldaorl/lda-orl
공식 코드A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning
https://github.com/ldcq/ldcq
공식 코드Reasoning with Latent Diffusion in Offline Reinforcement Learning
https://github.com/leamarion/automated_gadget_discovery
공식 코드https://github.com/leanderdiazbone/discover
공식 코드DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
https://github.com/leaplabthu/famo2o
공식 코드Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
https://github.com/leaplabthu/moss
공식 코드A Mixture of Surprises for Unsupervised Reinforcement Learning
https://github.com/learnables/learn2learn
공식 코드https://github.com/learningbydoingcompetition/learningbydoing-comp
공식 코드https://github.com/leaveslei/obd
공식 코드https://github.com/lebeaugroup/diffalignrl
공식 코드Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows
https://github.com/leitou/rl-based-mtd
공식 코드RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT
https://github.com/lemerleau/CubicPuzzle
공식 코드Approximately Optimal Search on a Higher-dimensional Sliding Puzzle
https://github.com/lemuria-wchen/dxformer
공식 코드https://github.com/lemutisme/dr-sac
공식 코드DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
https://github.com/leokan92/optimal-stopping-cnn
공식 코드https://github.com/leonardoamado/gr_from_demonstration
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.