Model-based Reinforcement Learning
195 papers with code • 0 benchmarks • 1 datasets
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Libraries
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POPGym
Most implemented papers
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
younggyoseo/CaDM
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ICML 2020
Model-based reinforcement learning (RL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environment's dynamics.
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
chandar-lab/LoCA
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NeurIPS 2020
For example, the common single-task sample-efficiency metric conflates improvements due to model-based learning with various other aspects, such as representation learning, making it difficult to assess true progress on model-based RL.
Understanding and Mitigating the Limitations of Prioritized Experience Replay
Prioritized Experience Replay (ER) has been empirically shown to improve sample efficiency across many domains and attracted great attention; however, there is little theoretical understanding of why such prioritized sampling helps and its limitations.
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
In this paper, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically.
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
siemens/industrialbenchmark
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This paves the way for new research directions, e. g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.
Online and Offline Reinforcement Learning by Planning with a Learned Model
DHDev0/Muzero-unplugged
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NeurIPS 2021
Combining Reanalyse with the MuZero algorithm, we introduce MuZero Unplugged, a single unified algorithm for any data budget, including offline RL.
Gradient Information Matters in Policy Optimization by Back-propagating through Model
Then we proposed a two-model-based learning method to control the prediction error and the gradient error.
Planning in Stochastic Environments with a Learned Model
opendilab/LightZero
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ICLR 2022
However, previous instantiations of this approach were limited to the use of deterministic models.
Planning with Diffusion for Flexible Behavior Synthesis
jannerm/diffuser
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Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers.
The Effectiveness of World Models for Continual Reinforcement Learning
World models power some of the most efficient reinforcement learning algorithms.
