Robust Locally-Linear Controllable Embedding
Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi

TL;DR
This paper introduces a robust locally-linear controllable embedding (RCE) model that improves upon previous methods by directly modeling predictive densities and incorporating structures for noise robustness, enhancing control in noisy environments.
Contribution
The paper proposes a new RCE model with a structured generative process and a variational approximation that accounts for future observations, improving robustness over existing methods.
Findings
RCE outperforms E2C in noisy dynamics scenarios.
The model provides more accurate embeddings under noise.
Experimental results demonstrate significant improvements.
Abstract
Embed-to-control (E2C) is a model for solving high-dimensional optimal control problems by combining variational auto-encoders with locally-optimal controllers. However, the E2C model suffers from two major drawbacks: 1) its objective function does not correspond to the likelihood of the data sequence and 2) the variational encoder used for embedding typically has large variational approximation error, especially when there is noise in the system dynamics. In this paper, we present a new model for learning robust locally-linear controllable embedding (RCE). Our model directly estimates the predictive conditional density of the future observation given the current one, while introducing the bottleneck between the current and future observations. Although the bottleneck provides a natural embedding candidate for control, our RCE model introduces additional specific structures in the…
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Taxonomy
TopicsModel Reduction and Neural Networks · Control Systems and Identification · Reinforcement Learning in Robotics
