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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/online-dt
공식 코드https://github.com/facebookresearch/ssorl
공식 코드Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
https://github.com/felix-thu/AlignIQL
공식 코드AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimization
https://github.com/felix-thu/DiffCPS
공식 코드DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
https://github.com/g0k0ururi/crop
공식 코드CROP: Conservative Reward for Model-based Offline Policy Optimization
https://github.com/google-research/google-research
공식 코드https://github.com/google/trax/tree/master/trax/models/reformer
공식 코드https://github.com/hanjuku-kaso/awesome-offline-rl
공식 코드CORL: Research-oriented Deep Offline Reinforcement Learning Library
https://github.com/hmomin/networkgym
공식 코드https://github.com/huawei-noah/HEBO/tree/master/PMDB
공식 코드Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
https://github.com/huawei-noah/hebo
공식 코드Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
https://github.com/huey0528/icml24didi
공식 코드DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation
https://github.com/idiap/fast-transformers
공식 코드Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
https://github.com/ikostrikov/implicit_q_learning
공식 코드https://github.com/imoneoi/RSP_JAX
공식 코드https://github.com/jihwan-jeong/cbop
공식 코드Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
https://github.com/kaichiuwong/rlhps
공식 코드Weak Human Preference Supervision For Deep Reinforcement Learning
https://github.com/kzl/decision-transformer
공식 코드Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/lastTarnished/Hypyercud-Policy-Regularization
공식 코드Hypercube Policy Regularization Framework for Offline Reinforcement Learning
https://github.com/lasttarnished/gpc-sac
공식 코드Grid-Mapping Pseudo-Count Constraint for Offline Reinforcement Learning
https://github.com/ldcq/ldcq
공식 코드Reasoning with Latent Diffusion in Offline Reinforcement Learning
https://github.com/leaplabthu/famo2o
공식 코드Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
https://github.com/leaveslei/obd
공식 코드https://github.com/liuzuxin/dsrl
공식 코드Datasets and Benchmarks for Offline Safe Reinforcement Learning
https://github.com/liuzuxin/fsrl
공식 코드Datasets and Benchmarks for Offline Safe Reinforcement Learning
https://github.com/liuzuxin/osrl
공식 코드Datasets and Benchmarks for Offline Safe Reinforcement Learning
https://github.com/ltlhuuu/PSEC
공식 코드https://github.com/ltlhuuu/a2pr
공식 코드Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
https://github.com/lucascjysdl/offline-rl-kit
공식 코드Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning
https://github.com/marvinalles/c-lap
공식 코드Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
https://github.com/meowatthemoon/decisionmamba
공식 코드https://github.com/meowatthemoon/hierarchicaldecisionmamba
공식 코드https://github.com/mhngu23/beyond-the-known-decision-making-with-counterfactual1-reasoning-decision-transformer
공식 코드Beyond the Known: Decision Making with Counterfactual Reasoning Decision Transformer
https://github.com/microsoft/atac
공식 코드Adversarially Trained Actor Critic for Offline Reinforcement Learning
https://github.com/mohammadrezanakhaei/relce
공식 코드Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.