https://github.com/kindredresearch/arp
공식 코드Autoregressive Policies for Continuous Control Deep Reinforcement Learning
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/kinjalbasu/explorer
공식 코드EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
https://github.com/kishanpb/RobustRL
공식 코드Sample Complexity of Robust Reinforcement Learning with a Generative Model
https://github.com/kishanpb/offpolicyevaluation_informationborrowing_contextswitching
공식 코드Off-Policy Evaluation Using Information Borrowing and Context-Based Switching
https://github.com/kli58/ROIAL
공식 코드https://github.com/kmc0207/Stableprompt
공식 코드StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models
https://github.com/kngwyu/infomax-option-critic
공식 코드https://github.com/kngwyu/rogue-gym
공식 코드Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning
https://github.com/kngwyu/rogue-gym-agents-cog19
공식 코드Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning
https://github.com/knoveleng/open-rs
공식 코드Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
https://github.com/knowledgedefinednetworking/DRL-GNN
공식 코드Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
https://github.com/knowledgetechnologyuhh/goal_conditioned_RL_baselines
공식 코드https://github.com/knowledgetechnologyuhh/hipss
공식 코드Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics
https://github.com/kokolerk/ton
공식 코드Think or Not? Selective Reasoning via Reinforcement Learning for Vision-Language Models
https://github.com/konatanaya/geometric-actor-critic
공식 코드GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks
https://github.com/koulanurag/dream-and-search
공식 코드Dream and Search to Control: Latent Space Planning for Continuous Control
https://github.com/krishanrana/reskill
공식 코드https://github.com/kristian-georgiev/sgmrl
공식 코드On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
https://github.com/krlgroup/neuralrewardmachines
공식 코드https://github.com/krystalan/drt
공식 코드ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement Learning
https://github.com/kschmeckpeper/rl_with_videos
공식 코드Reinforcement Learning with Videos: Combining Offline Observations with Interaction
https://github.com/ksenia-konyushkova/LAL-RL
공식 코드https://github.com/kumarabhish3k/Rainbow-DQN-for-Contention-Window-design
공식 코드https://github.com/kwai-keye/keye
공식 코드https://github.com/kwai/DouZero
공식 코드DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
https://github.com/kxfan2002/sophiavl-r1
공식 코드SophiaVL-R1: Reinforcing MLLMs Reasoning with Thinking Reward
https://github.com/kyonofx/tia
공식 코드https://github.com/kzbikowski/ProcessGym
공식 코드Deep Reinforcement Learning for Resource Allocation in Business Processes
https://github.com/kzl/decision-transformer
공식 코드Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/kzl/lifelong_rl
공식 코드https://github.com/labicon/curricullm
공식 코드https://github.com/lamda-bbo/macro-regulator
공식 코드Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer
https://github.com/lamda-bbo/madac
공식 코드https://github.com/lamda-rl/imaginebench
공식 코드ImagineBench: Evaluating Reinforcement Learning with Large Language Model Rollouts
https://github.com/lamda-rl/prdc
공식 코드Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
https://github.com/lamsieh/DQN
공식 코드Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.