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Model-based Reinforcement Learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ramp-kits/rl_simulator

공식 코드

Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?

https://github.com/ran-weii/objective_mismatch_papers

공식 코드

A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning

https://github.com/realwenlongwang/drama

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Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient

https://github.com/remosasso/psdrl

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Posterior Sampling for Deep Reinforcement Learning

https://github.com/resibots/kaushik_2020_famle

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Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors

https://github.com/rmbpo-eval/rmbpo-eval

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Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model

https://github.com/romina72/paml

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Policy-Aware Model Learning for Policy Gradient Methods

https://github.com/roosephu/slbo

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Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

https://github.com/ryeii/CLUE

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A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control

https://github.com/ryeii/veri_hvac

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Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control

https://github.com/sarodyatawatta/hintrl

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Hint assisted reinforcement learning: an application in radio astronomy

https://github.com/seanjparker/rlflow

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RLFlow: Optimising Neural Network Subgraph Transformation with World Models

https://github.com/seanrsinclair/AdaptiveQLearning

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Adaptive Discretization for Model-Based Reinforcement Learning

https://github.com/sebascuri/hucrl

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Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

https://github.com/seohongpark/pma

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Predictable MDP Abstraction for Unsupervised Model-Based RL

https://github.com/shagunsodhani/consistent-dynamics

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Learning Powerful Policies by Using Consistent Dynamics Model

https://github.com/sharadmv/parasol

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SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning

https://github.com/shentao-yang/ampl_neurips2022

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A Unified Framework for Alternating Offline Model Training and Policy Learning

https://github.com/si0wang/pdml

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Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy

https://github.com/siemens/industrialbenchmark

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Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

https://github.com/siemens/policy_search_bb-alpha

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Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

https://github.com/sisl/mechamodlearn

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A General Framework for Structured Learning of Mechanical Systems

https://github.com/skezle/continual-dreamer

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The Effectiveness of World Models for Continual Reinforcement Learning

https://github.com/stratosphereips/meme_malware_rl

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The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning

https://github.com/syscv/trafficbots

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TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction

https://github.com/tensorflow/tensor2tensor

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Model-Based Reinforcement Learning for Atari

https://github.com/thanard/me-trpo

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Model-Ensemble Trust-Region Policy Optimization

https://github.com/thobotics/RoMBRL

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Bayes-Adaptive Deep Model-Based Policy Optimisation

https://github.com/thomasbi1/cyberrunner

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Sample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning

https://github.com/thuml/CompilerDream

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CompilerDream: Learning a Compiler World Model for General Code Optimization

https://github.com/thuml/ContextWM

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Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning

https://github.com/thuml/harmonydream

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HarmonyDream: Task Harmonization Inside World Models

https://github.com/thuml/iVideoGPT

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iVideoGPT: Interactive VideoGPTs are Scalable World Models

https://github.com/tmoer/multimodal_varinf

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Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning

https://github.com/tsutikgiau/valuememorygraph

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Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning

https://github.com/tung-nd/TPC-tensorflow

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Temporal Predictive Coding For Model-Based Planning In Latent Space

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.