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/linenus/DRL-For-imbalanced-Classification
공식 코드https://github.com/linhlpv/DADS
공식 코드https://github.com/linjunjie99/rl-llm-dt
공식 코드RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM Enhancement
https://github.com/linkangheng/pr1
공식 코드Perception-R1: Pioneering Perception Policy with Reinforcement Learning
https://github.com/linkpark/metarl-offloading
공식 코드Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
https://github.com/linlinlin97/mediationrl
공식 코드A Reinforcement Learning Framework for Dynamic Mediation Analysis
https://github.com/lionelblonde/giwr-pytorch
공식 코드Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learning
https://github.com/liqimai/flatland-marl
공식 코드https://github.com/liqing-ustc/NGS
공식 코드https://github.com/liqunzhao/a-barrier-lyapunov-actor-critic-reinforcement-learning-approach-for-safe-and-stable-control
공식 코드Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic Approach
https://github.com/liqunzhao/neural-ordinary-differential-equations-based-lyapunov-barrier-actor-critic-nlbac
공식 코드Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations
https://github.com/liruiw/GA-DDPG
공식 코드Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
https://github.com/lis-epfl/apg_trajectory_tracking
공식 코드https://github.com/lisaoakley/flipit-simulation
공식 코드QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security Game
https://github.com/lisaoakley/gym-flipit
공식 코드QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security Game
https://github.com/lisifra96/improved_rl_algorithm_mmimo_radar
공식 코드Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars
https://github.com/litingfeng/localization-by-ordembed
공식 코드Learning Transferable Reward for Query Object Localization with Policy Adaptation
https://github.com/liujch1998/rainier
공식 코드Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering
https://github.com/liuruoze/hiernet-sc2
공식 코드On Efficient Reinforcement Learning for Full-length Game of StarCraft II
https://github.com/liuruoze/mini-AlphaStar
공식 코드On Efficient Reinforcement Learning for Full-length Game of StarCraft II
https://github.com/liuruoze/thought-sc2-model-experiment
공식 코드Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning
https://github.com/liushunyu/opt
공식 코드Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
https://github.com/liuxhym/edis
공식 코드Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
https://github.com/liuxin0824/comsd
공식 코드ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discovery
https://github.com/liuxin0824/crptpro
공식 코드Cross-domain Random Pre-training with Prototypes for Reinforcement Learning
https://github.com/liuyuqi123/complexurbanscenarios
공식 코드A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios
https://github.com/liuzuxin/cvpo-safe-rl
공식 코드Constrained Variational Policy Optimization for Safe Reinforcement Learning
https://github.com/liuzuxin/dsrl
공식 코드Datasets and Benchmarks for Offline Safe Reinforcement Learning
https://github.com/liuzuxin/fsrl
공식 코드Datasets and Benchmarks for Offline Safe Reinforcement Learning
https://github.com/liuzuxin/osrl
공식 코드Constrained Decision Transformer for Offline Safe Reinforcement Learning
https://github.com/liuzuxin/safe-mbrl
공식 코드Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method
https://github.com/liuzuxin/safe-rl-robustness
공식 코드On the Robustness of Safe Reinforcement Learning under Observational Perturbations
https://github.com/liyheng/GPO
공식 코드https://github.com/liyuan9988/IVOPEwithACME
공식 코드On Instrumental Variable Regression for Deep Offline Policy Evaluation
https://github.com/liza-tennant/moral_choice_dyadic
공식 코드Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.