https://github.com/qiwang067/CoWorld
공식 코드Making Offline RL Online: Collaborative World Models for Offline Visual 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/qiwang98/videorft
공식 코드VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning
https://github.com/qixinwangcpslab/rl-testing-new
공식 코드Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning Programs
https://github.com/qlan3/Explorer
공식 코드Randomized Exploration for Reinforcement Learning with General Value Function Approximation
https://github.com/qlan3/MeDQN
공식 코드Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation
https://github.com/qlan3/quantumexplorer
공식 코드https://github.com/quangpham2006/SmallPlan
공식 코드https://github.com/quantum-jwjae/rl2lqs
공식 코드Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
https://github.com/quantumiracle/Cascading-Decision-Tree
공식 코드CDT: Cascading Decision Trees for Explainable Reinforcement Learning
https://github.com/quantumiracle/consistency_model_for_reinforcement_learning
공식 코드Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
https://github.com/quanvuong/Supervised_Policy_Update
공식 코드https://github.com/quanvuong/domain_randomization
공식 코드https://github.com/quark0/darts
공식 코드https://github.com/quasimetric-learning/quasimetric-rl
공식 코드Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
https://github.com/quasimetric-learning/torch-quasimetric
공식 코드Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings
https://github.com/qutech-delft/qgym
공식 코드qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation
https://github.com/qyyin/Reinforce4ZP
공식 코드Deep Reinforcement Learning for Chinese Zero pronoun Resolution
https://github.com/r4wd3r/llms-are-acd
공식 코드https://github.com/raaslab/rl_multi_agent
공식 코드Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring
https://github.com/ragen-ai/ragen
공식 코드RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
https://github.com/raghudiddigi/Off-Policy-Convergent-Algorithm
공식 코드https://github.com/rahul13ramesh/SuccessorOptions
공식 코드Successor Options: An Option Discovery Framework for Reinforcement Learning
https://github.com/rahular/coref-rl
공식 코드Rewarding Coreference Resolvers for Being Consistent with World Knowledge
https://github.com/rail-berkeley/d4rl
공식 코드https://github.com/rail-berkeley/offline_rl
공식 코드https://github.com/rail-berkeley/rlkit
공식 코드https://github.com/rail-berkeley/rlkit/blob/master/docs/SkewFit.md
공식 코드Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
https://github.com/rail-berkeley/softlearning
공식 코드https://github.com/rail-berkeley/supe
공식 코드Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration
https://github.com/raise-lab/soc-nav-training
공식 코드Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation
https://github.com/rajammanabrolu/KG-A2C
공식 코드Graph Constrained Reinforcement Learning for Natural Language Action Spaces
https://github.com/rajammanabrolu/KG-DQN
공식 코드Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning
https://github.com/rajammanabrolu/Q-BERT
공식 코드How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure Agents
https://github.com/rajcscw/nlp-gym
공식 코드NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing Tasks
https://github.com/rajghugare19/stitching-is-combinatorial-generalisation
공식 코드Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of View
https://github.com/ral94/rlcbm
공식 코드Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.