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/jangikim2/Offline-to-online-RL-with-non-monolithic-exploration-methodology
공식 코드A Non-Monolithic Policy Approach of Offline-to-Online Reinforcement Learning
https://github.com/jangikim2/an-autonomous-non-monolithic-agent-with-multi-mode-exploration-based-on-options-framework
공식 코드An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options Framework
https://github.com/janscholten/ppmp
공식 코드https://github.com/janstenner/distributedconvrl-pde-control
공식 코드Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning
https://github.com/jaraujo98/SinglePartitionAdaptiveQLearning
공식 코드https://github.com/jaromiru/cwcf
공식 코드Classification with Costly Features using Deep Reinforcement Learning
https://github.com/jaromiru/sr-drl
공식 코드https://github.com/jarvisustc/doctoragent-rl
공식 코드https://github.com/jasonjiaxiangli/reward_learning_sft
공식 코드https://github.com/jasonma2016/gofar
공식 코드How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression
https://github.com/jasonma2016/smodice
공식 코드Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
https://github.com/jasonyux/krls
공식 코드https://github.com/jasonzhang929/BVFT_empirical_experiments
공식 코드Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
https://github.com/jayaneetha/Zeta-Policy
공식 코드A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition
https://github.com/jaylee0301/outpace_official
공식 코드https://github.com/jazlab/moog.github.io
공식 코드https://github.com/jbkjr/train-procgen-pytorch
공식 코드https://github.com/jbr-ai-labs/mamba
공식 코드https://github.com/jcoreyes/OP3
공식 코드Entity Abstraction in Visual Model-Based Reinforcement Learning
https://github.com/jderiu/metric-robustness
공식 코드Probing the Robustness of Trained Metrics for Conversational Dialogue Systems
https://github.com/jeremy-costello/rl-for-topic-models
공식 코드https://github.com/jermcmahan/attack-defense
공식 코드https://github.com/jerrodparker20/adaptive-transformers-in-rl
공식 코드https://github.com/jerry99s/NonZeroSum_Driving
공식 코드Non-zero-sum Game Control for Multi-vehicle Driving via Reinforcement Learning
https://github.com/jesbu1/hidio
공식 코드Hierarchical Reinforcement Learning By Discovering Intrinsic Options
https://github.com/jesse-michael-han/neuro-cadical
공식 코드https://github.com/jessefarebro/dqn-ale
공식 코드https://github.com/jesserem/MODCMAC
공식 코드https://github.com/jesserem/Offline-LD
공식 코드Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
https://github.com/jf-hu/dre-marl
공식 코드Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning
https://github.com/jhare96/reinforcement-learning
공식 코드https://github.com/jhejna/cpl
공식 코드Contrastive Preference Learning: Learning from Human Feedback without RL
https://github.com/jhl-hust/titer
공식 코드TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting
https://github.com/jhl-hust/vsr-lkh-v2
공식 코드Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problems
https://github.com/jhoon-cho/mbtl
공식 코드Model-Based Transfer Learning for Contextual Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.