https://github.com/zchuning/latco
공식 코드Model-Based Reinforcement Learning via Latent-Space Collocation
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/zeyubian/2feope
공식 코드https://github.com/zeyuzhang1901/unified-off-policy-ltr-neurips2023
공식 코드Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
https://github.com/zhanzxy5/MOPP
공식 코드https://github.com/zhaoyi11/adaptive_bc
공식 코드Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
https://github.com/zhaoyizhou1123/mbrcsl
공식 코드https://github.com/zhejz/trafficbots
공식 코드TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
https://github.com/zhendong-wang/diffusion-policies-for-offline-rl
공식 코드Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
https://github.com/zheng0428/more_
공식 코드MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces
https://github.com/zhenghuazx/BayesianLRPolicySearch
공식 코드https://github.com/zhenghuazx/Policy-Optimization-in-Dynamic-Bayesian-Network
공식 코드Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes
https://github.com/zhengyinan-air/fisor
공식 코드Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
https://github.com/zhouzypaul/wsrl
공식 코드Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data
https://github.com/zifanwu/plan-to-predict
공식 코드Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning
https://github.com/zihaosheng/traffic-expertise-rl
공식 코드https://gitlab.com/hdonancio/pumpscheduling
공식 코드The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning
https://agit.ai/Polixir/OfflineRL
https://github.com/2023-MindSpore-1/ms-code-220/tree/main/pmdb
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/decision_transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/AIRLegend/ChessRL
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/Amadeus979/decision-transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/Archibald-Lafraik/pipps
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
https://github.com/Asap7772/OfflineRlWorkflow
A Workflow for Offline Model-Free Robotic Reinforcement Learning
https://github.com/BFAnas/BranchingDQN_keras
Action Branching Architectures for Deep Reinforcement Learning
https://github.com/BY571/CQL
https://github.com/BY571/Implicit-Q-Learning
https://github.com/BY571/MBPO
https://github.com/Bam4d/Neural-Game-Engine
Neural Game Engine: Accurate learning of generalizable forward models from pixels
https://github.com/BertilBraun/ChessBot
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/ByMic/PETS
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/Cejkis/I2A-Cejkis
Imagination-Augmented Agents for Deep Reinforcement Learning
https://github.com/ComarPers922/AI-based-3D-Game-Simulator
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/DHDev0/Muzero-unplugged
Online and Offline Reinforcement Learning by Planning with a Learned Model
https://github.com/DanielTakeshi/DCUR
DCUR: Data Curriculum for Teaching via Samples with Reinforcement Learning
https://github.com/Delavari-Alireza/Decision_Transformer_Sequence-Modeling_Deep_Reinforcement_Learning
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/DrJupiter/Deep-Reinforcement-Rubiks-Cube-Solver
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.