Jul 10, 2018 · This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees.
Dec 20, 2018 · This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees.
Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL.
This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees.
A novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees is introduced and a meta-algorithm with a ...
This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algo- rithms with theoretical guarantees. We design a meta- ...
Feb 15, 2021 · Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL.
Aug 28, 2021 · This is the TensorFlow implementation for the paper Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees.
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In this paper, we introduce a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees, and a practical ...
Aug 8, 2021 · Bibliographic details on Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees.