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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/kevinhuang8/cem-gd

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CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning

https://github.com/kishanpb/RobustRL

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Sample Complexity of Robust Reinforcement Learning with a Generative Model

https://github.com/koulanurag/dream-and-search

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Dream and Search to Control: Latent Space Planning for Continuous Control

https://github.com/kyonofx/tia

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Learning Task Informed Abstractions

https://github.com/lace-lab/bounding-box

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Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning

https://github.com/lapisrocks/languageagenttreesearch

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Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

https://github.com/learningbydoingcompetition/learningbydoing-comp

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Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning

https://github.com/liuqh16/mazero

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Efficient Multi-agent Reinforcement Learning by Planning

https://github.com/liuzuxin/safe-mbrl

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Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method

https://github.com/lucascjysdl/offline-rl-kit

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Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning

https://github.com/martius-lab/iCEM

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Sample-efficient Cross-Entropy Method for Real-time Planning

https://github.com/matthewwicker/bnnreachavoid

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Probabilistic Reach-Avoid for Bayesian Neural Networks

https://github.com/mb-29/exploration

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FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems

https://github.com/mcgillmrl/robot_learning

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Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning

https://github.com/menggedu/discover

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DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning

https://github.com/michelllepan/neural-coprocessors

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Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation

https://github.com/mig-zh/OODP

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Object-Oriented Dynamics Predictor

https://github.com/mila-iqia/conscious-planning

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A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning

https://github.com/mila-iqia/skipper

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Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning

https://github.com/ml-postech/gm-vae

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Hyperbolic VAE via Latent Gaussian Distributions

https://github.com/mlpc-ucsd/xtra

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On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning

https://github.com/mrjun123/DPETS

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Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling

https://github.com/mxu34/mbrl-gpmm

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Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

https://github.com/nicoladainese96/code-world-models

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Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search

https://github.com/nicolinho/dutd

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Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting

https://github.com/nzolman/sindy-rl

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SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

https://github.com/pairlab/vagram

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Value Gradient weighted Model-Based Reinforcement Learning

https://github.com/panmt/mbrl_with_isolated_imaginations

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Model-Based Reinforcement Learning with Isolated Imaginations

https://github.com/panmt/vid2act

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Model-Based Reinforcement Learning with Multi-Task Offline Pretraining

https://github.com/pbecker93/vrkn

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On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning

https://github.com/pengyang7881187/sicmdp-new

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Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning

https://github.com/penn-pal-lab/robot-aware-control

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Know Thyself: Transferable Visual Control Policies Through Robot-Awareness

https://github.com/peteanderson80/Matterport3DSimulator

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Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

https://github.com/pikaju/muzero-g

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Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision

https://github.com/proppo/pipps_demo

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PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

https://github.com/purewater0901/BSMPC

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Bisimulation metric for Model Predictive Control

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