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Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time
In this paper, we introduce Hamilton-Jacobi-Bellman (HJB) equations for ...
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Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation
The control of nonlinear dynamical systems remains a major challenge for...
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On Applications of Bootstrap in Continuous Space Reinforcement Learning
In decision making problems for continuous state and action spaces, line...
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Structured Neural Network Dynamics for Model-based Control
We present a structured neural network architecture that is inspired by ...
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SACBP: Belief Space Planning for Continuous-Time Dynamical Systems via Stochastic Sequential Action Control
We propose a novel belief space planning technique for continuous dynami...
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A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation
One of the challenges in model-based control of stochastic dynamical sys...
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Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is inte...
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Unsupervised Real-Time Control through Variational Empowerment
We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control. Empowerment, being the channel capacity between actions and states, maximises the influence of an agent on its near future. It has been shown to be a good model of biological behaviour in the absence of an extrinsic goal. But empowerment is also prohibitively hard to compute, especially in nonlinear continuous spaces. We introduce an efficient, amortised method for learning empowerment-maximising policies. We demonstrate that our algorithm can reliably handle continuous dynamical systems using system dynamics learned from raw data. The resulting policies consistently drive the agents into states where they can use their full potential.
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