Papers with Code 아카이브
Reinforcement Learning (RL)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 2
- 보존된 평가 기록
- 3
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/morningmoni/RL-MMR
공식 코드Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning
https://github.com/morningmoni/TaxoRL
공식 코드End-to-End Reinforcement Learning for Automatic Taxonomy Induction
https://github.com/motokiomura/annealed-q-learning
공식 코드https://github.com/mpd37/pilco-matlab
공식 코드Gaussian Processes for Data-Efficient Learning in Robotics and Control
https://github.com/mr-benjie/idrl
공식 코드https://github.com/mradaideh/neorl
공식 코드NEORL: NeuroEvolution Optimization with Reinforcement Learning
https://github.com/mrkulk/hierarchical-deep-RL
공식 코드https://github.com/mrucker/kpirl-kla
공식 코드Reward Shaping for Human Learning via Inverse Reinforcement Learning
https://github.com/msaveski/counterfactual-peer-review
공식 코드Counterfactual Evaluation of Peer-Review Assignment Policies
https://github.com/mselser95/optimal-market-making
공식 코드https://github.com/mszulc913/acerac
공식 코드A framework for reinforcement learning with autocorrelated actions
https://github.com/mttga/purejaxql
공식 코드https://github.com/mudita11/DE-DDQN
공식 코드Deep Reinforcement Learning Based Parameter Control in Differential Evolution
https://github.com/multi-swe-bench/experiments
공식 코드Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving
https://github.com/multi-swe-bench/mopenhands
공식 코드Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving
https://github.com/multi-swe-bench/multi-swe-bench
공식 코드Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving
https://github.com/mushfiqur11/simpledc-dataset
공식 코드https://github.com/muttimirco/alphamepol
공식 코드Unsupervised Reinforcement Learning in Multiple Environments
https://github.com/mwalton/ToM-hanabi-neurips19
공식 코드https://github.com/mweiss17/SEVN
공식 코드Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
https://github.com/mwydmuch/ViZDoom
공식 코드ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
https://github.com/mxu34/mbrl-gpmm
공식 코드Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
https://github.com/myfatemi04/rl-deceptive-graph-planning
공식 코드Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks
https://github.com/myrepositories-hub/mutual-distillation-policy-optimization
공식 코드Representation Convergence: Mutual Distillation is Secretly a Form of Regularization
https://github.com/myyura/minimax-dqn
공식 코드M$^2$DQN: A Robust Method for Accelerating Deep Q-learning Network
https://github.com/myzwisc/PPRL_NeurIPS19
공식 코드Policy Poisoning in Batch Reinforcement Learning and Control
https://github.com/mzho7212/LICA
공식 코드Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
https://github.com/nakamotoo/cal-ql
공식 코드Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
https://github.com/nakanakakosuke/valt_sac
공식 코드https://github.com/nanxintin/StarCraft-AI
공식 코드StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
https://github.com/natashamjaques/neural_chat
공식 코드Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
https://github.com/nathangrinsztajn/DAG-scheduling
공식 코드Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
https://github.com/naumix/BiggerRegularizedOptimistic
공식 코드Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control
https://github.com/navarog/cross-validated-ope
공식 코드https://github.com/naver-ai/rethinking-proxy-reward
공식 코드Rethinking the Role of Proxy Rewards in Language Model Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.