https://github.com/thu-rllab/lare
공식 코드Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning
Papers with Code 아카이브
Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/thudm/treerl
공식 코드TreeRL: LLM Reinforcement Learning with On-Policy Tree Search
https://github.com/thuml/RLVR-World
공식 코드RLVR-World: Training World Models with Reinforcement Learning
https://github.com/thuml/SPOT
공식 코드Supported Policy Optimization for Offline Reinforcement Learning
https://github.com/thuml/iVideoGPT
공식 코드https://github.com/thuzouwenjun/mupo
공식 코드https://github.com/thward/neural_agent
공식 코드A Threshold-based Scheme for Reinforcement Learning in Neural Networks
https://github.com/thxsxth/POMDP_RLSepsis
공식 코드https://github.com/tianheyu927/PCGrad
공식 코드https://github.com/tianhongdai/div-hindsight
공식 코드Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay
https://github.com/tianjunz/hir
공식 코드The Wisdom of Hindsight Makes Language Models Better Instruction Followers
https://github.com/tianjunz/tempera
공식 코드https://github.com/tiantan522/PINs
공식 코드Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning
https://github.com/tianyady/fedslate
공식 코드FedSlate:A Federated Deep Reinforcement Learning Recommender System
https://github.com/tianyuan1wang/seqanglerl
공식 코드Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning
https://github.com/tianyucodings/diffusion_trusted_q_learning
공식 코드Diffusion Policies creating a Trust Region for Offline Reinforcement Learning
https://github.com/tidiane-camaret/contextual_rl_zero_shot
공식 코드Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning
https://github.com/tiencapham/ife
공식 코드https://github.com/tilmanimmisch/gwrr
공식 코드https://github.com/timo-verstraeten/fwpi-experiments
공식 코드Fleet Control using Coregionalized Gaussian Process Policy Iteration
https://github.com/timotheemathieu/adaptive_stopping_mc_rl
공식 코드AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
https://github.com/tinker-twins/autorace-simulator
공식 코드Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
https://github.com/tinkoff-ai/CORL
공식 코드CORL: Research-oriented Deep Offline Reinforcement Learning Library
https://github.com/tinkoff-ai/cnf
공식 코드Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows
https://github.com/tirthankar95/numberlearninginchildren_rl_nlp
공식 코드https://github.com/tliu1997/rnac
공식 코드Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
https://github.com/tmdt-buw/schlably
공식 코드schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
https://github.com/tmoer/multimodal_varinf
공식 코드Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning
https://github.com/tmoer/return_distribution_exploration
공식 코드The Potential of the Return Distribution for Exploration in RL
https://github.com/tobiaslv/rad
공식 코드Conformal Symplectic Optimization for Stable Reinforcement Learning
https://github.com/tolgaok/offlinerlviaio
공식 코드https://github.com/tom1042roboai/aics_2022_causalcf
공식 코드Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
https://github.com/tom1042roboai/causalcf
공식 코드Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
https://github.com/tombewley/TripleTree
공식 코드TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments
https://github.com/tomdbar/eco-dqn
공식 코드Exploratory Combinatorial Optimization with Reinforcement Learning
https://github.com/tomjur/ModelBasedDDPG
공식 코드Harnessing Reinforcement Learning for Neural Motion Planning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.