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/danhrmti/ecrl
공식 코드Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
https://github.com/danialebrat/lusifer
공식 코드Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systems
https://github.com/danielegammelli/graph-meta-rl-for-amod
공식 코드Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
https://github.com/danieleugenioneves/comper-release-results
공식 코드Improving Experience Replay through Modeling of Similar Transitions' Sets
https://github.com/danielpalen/value_expansion
공식 코드Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
https://github.com/danielwillemsen/mambpo
공식 코드MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models
https://github.com/danijar/daydreamer
공식 코드https://github.com/danijar/dreamer
공식 코드https://github.com/danijar/dreamerv3
공식 코드https://github.com/danilprov/cmdp
공식 코드An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning
https://github.com/daochenzha/Meta-AAD
공식 코드Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning
https://github.com/daochenzha/SSRL
공식 코드Simplifying Deep Reinforcement Learning via Self-Supervision
https://github.com/daochenzha/autosmote
공식 코드Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
https://github.com/daochenzha/dreamshard
공식 코드DreamShard: Generalizable Embedding Table Placement for Recommender Systems
https://github.com/daqingliu/CAVP
공식 코드Context-Aware Visual Policy Network for Sequence-Level Image Captioning
https://github.com/darkefyre/sepsisrl
공식 코드https://github.com/darl-libsignal/promptgat
공식 코드Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
https://github.com/darl-libsignal/ugat
공식 코드https://github.com/darshitajain/crc-rl-
공식 코드CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement Learning
https://github.com/dasgringuen/assetto_corsa_gym
공식 코드A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
https://github.com/data-and-decision-lab/defix
공식 코드https://github.com/data-and-decision-lab/self-improving-RL
공식 코드https://github.com/data-science-in-mechanical-engineering/entropy_robustness
공식 코드Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization
https://github.com/data-science-in-mechanical-engineering/polishing-cheq
공식 코드CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic Polishing
https://github.com/data-science-in-mechanical-engineering/sae-rl
공식 코드https://github.com/datake/robustdistrl
공식 코드https://github.com/datake/sinkhorndistrl
공식 코드Distributional Reinforcement Learning with Regularized Wasserstein Loss
https://github.com/datamllab/rlcard
공식 코드https://github.com/david-abel/state_abstraction
공식 코드https://github.com/david-lindner/idrl
공식 코드Information Directed Reward Learning for Reinforcement Learning
https://github.com/davidbrandfonbrener/onestep-rl
공식 코드https://github.com/davidbrandfonbrener/rcsl-paper
공식 코드When does return-conditioned supervised learning work for offline reinforcement learning?
https://github.com/dawnhh/dlsr-pytorch
공식 코드Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution
https://github.com/dbaranchuk/nns-meets-deep-rl
공식 코드Towards Similarity Graphs Constructed by Deep Reinforcement Learning
https://github.com/dbsxodud-11/PAG
공식 코드Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.