https://github.com/junming-yang/moan
공식 코드Model-based Offline Policy Optimization with Adversarial Network
Papers with Code 아카이브
General Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jwhj/oreo
공식 코드https://github.com/jzitovsky/sbv
공식 코드https://github.com/k4ntz/activation-functions
공식 코드Adaptive Rational Activations to Boost Deep Reinforcement Learning
https://github.com/kaesve/muzero
공식 코드https://github.com/kaixhin/gudrl
공식 코드All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL
https://github.com/kasunweerkoon/VAPOR
공식 코드VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning
https://github.com/kavosh8/Lip
공식 코드https://github.com/kchua/handful-of-trials
공식 코드Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/kenjyoung/model_generalization_code_supplement
공식 코드The Benefits of Model-Based Generalization in Reinforcement Learning
https://github.com/kevinhuang8/cem-gd
공식 코드CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
https://github.com/kevinzhou497/distcb
공식 코드The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
https://github.com/khanhptnk/iliad
공식 코드https://github.com/khatch31/laeo
공식 코드https://github.com/kishanpb/RobustRL
공식 코드Sample Complexity of Robust Reinforcement Learning with a Generative Model
https://github.com/koulanurag/dream-and-search
공식 코드Dream and Search to Control: Latent Space Planning for Continuous Control
https://github.com/kvfrans/cfgrl
공식 코드Diffusion Guidance Is a Controllable Policy Improvement Operator
https://github.com/kvfrans/fre
공식 코드Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
https://github.com/kyonofx/tia
공식 코드https://github.com/kzl/decision-transformer
공식 코드Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/lace-lab/bounding-box
공식 코드Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning
https://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/lapisrocks/languageagenttreesearch
공식 코드Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
https://github.com/larocs/offline-rl-suvey
공식 코드A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
https://github.com/lastTarnished/Hypyercud-Policy-Regularization
공식 코드Hypercube Policy Regularization Framework for Offline Reinforcement Learning
https://github.com/ldcq/ldcq
공식 코드Reasoning with Latent Diffusion in Offline Reinforcement Learning
https://github.com/learningbydoingcompetition/learningbydoing-comp
공식 코드https://github.com/lemutisme/dr-sac
공식 코드DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
https://github.com/lifelong-ml/offline-compositional-rl-datasets
공식 코드Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning
https://github.com/lingweizhu/fat2thin
공식 코드Fat-to-Thin Policy Optimization: Offline RL with Sparse Policies
https://github.com/linjc16/Rec-R1
공식 코드https://github.com/link-zju/orl-auditor
공식 코드ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
https://github.com/lionelblonde/giwr-pytorch
공식 코드Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learning
https://github.com/liuqh16/mazero
공식 코드https://github.com/liuzuxin/safe-mbrl
공식 코드Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.