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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/ed2-source-code/ed2

공식 코드

ED2: Environment Dynamics Decomposition World Models for Continuous Control

https://github.com/eleurent/highway-env

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Understanding and Mitigating the Limitations of Prioritized Experience Replay

https://github.com/erg0dic/transmon_public

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Sample-efficient Model-based Reinforcement Learning for Quantum Control

https://github.com/etalvitie/hdaggermc

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Self-Correcting Models for Model-Based Reinforcement Learning

https://github.com/evgenii-nikishin/omd

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Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation

https://github.com/facebookresearch/mbrl-lib

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MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

https://github.com/facebookresearch/modem

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MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations

https://github.com/facebookresearch/svg

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On the model-based stochastic value gradient for continuous reinforcement learning

https://github.com/fdeng18/dreamer-pro

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DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

https://github.com/filippoairaldi/mpcrl-cbf

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Probabilistically safe and efficient model-based Reinforcement Learning

https://github.com/filippoairaldi/mpcrl-for-ramp-metering

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Reinforcement Learning with Model Predictive Control for Highway Ramp Metering

https://github.com/gaoyinfeng/piwm

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Dream to Drive with Predictive Individual World Model

https://github.com/google-research/dads

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Dynamics-Aware Unsupervised Discovery of Skills

https://github.com/google-research/google-research

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Learning Abstract Models for Strategic Exploration and Fast Reward Transfer

https://github.com/google-research/world_models

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Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning

https://github.com/hanglai/autombpo

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On Effective Scheduling of Model-based Reinforcement Learning

https://github.com/hanglai/bmpo

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Bidirectional Model-based Policy Optimization

https://github.com/hardikparwana/foresee

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FORESEE: Prediction with Expansion-Compression Unscented Transform for Online Policy Optimization

https://github.com/harryzhangOG/salved

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Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions

https://github.com/homangab/gradcem

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Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization

https://github.com/janisgeise/mb_drl_for_accelerated_learning_from_cfd

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Model-based deep reinforcement learning for accelerated learning from flow simulations

https://github.com/jannerm/diffuser

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Planning with Diffusion for Flexible Behavior Synthesis

https://github.com/jbr-ai-labs/mamba

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Scalable Multi-Agent Model-Based Reinforcement Learning

https://github.com/jc043/cpl

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Predictive Experience Replay for Continual Visual Control and Forecasting

https://github.com/jcoreyes/OP3

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Entity Abstraction in Visual Model-Based Reinforcement Learning

https://github.com/jerry99s/NonZeroSum_Driving

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Non-zero-sum Game Control for Multi-vehicle Driving via Reinforcement Learning

https://github.com/jessefarebro/distributional-sr

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A Distributional Analogue to the Successor Representation

https://github.com/jity16/when-to-update-your-model-constrained-model-based-reinforcement-learning

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When to Update Your Model: Constrained Model-based Reinforcement Learning

https://github.com/jnousi/po4ao

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Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control

https://github.com/jrobine/twm

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Transformer-based World Models Are Happy With 100k Interactions

https://github.com/juancamilog/kusanagi

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Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning

https://github.com/juancamilog/robot_learning

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Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning

https://github.com/kaesve/muzero

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Visualizing MuZero Models

https://github.com/kavosh8/Lip

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Lipschitz Continuity in Model-based Reinforcement Learning

https://github.com/kchua/handful-of-trials

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Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

https://github.com/kenjyoung/model_generalization_code_supplement

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The Benefits of Model-Based Generalization in Reinforcement Learning

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