Model-based Reinforcement Learning
195 papers with code • 0 benchmarks • 1 datasets
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Latest papers with no code
A Note on Loss Functions and Error Compounding in Model-based Reinforcement Learning
This note clarifies some confusions (and perhaps throws out more) around model-based reinforcement learning and their theoretical understanding in the context of deep RL.
Active Learning for Control-Oriented Identification of Nonlinear Systems
Model-based reinforcement learning is an effective approach for controlling an unknown system.
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation
Model-based RL, by building a dynamic model of the robot, enables data reuse and transfer learning between tasks with the same robot and similar environment.
Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control
Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input.
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
The proposed agent realizes a version of divide-and-conquer-like strategy in dreaming.
When in Doubt, Think Slow: Iterative Reasoning with Latent Imagination
In an unfamiliar setting, a model-based reinforcement learning agent can be limited by the accuracy of its world model.
Model-Based Reinforcement Learning Control of Reaction-Diffusion Problems
Mathematical and computational tools have proven to be reliable in decision-making processes.
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
We also prove a lower bound to show that the additive dependence on $C$ is optimal.
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning
In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data.
Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning
We consider the problem of offline reinforcement learning where only a set of system transitions is made available for policy optimization.
