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arXiv · 2210.01801

Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation

Yannick Hogewind·Thiago D. Simao·Tal Kachman·Nils Jansen
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Abstract

We address the problem of safe reinforcement learning from pixel observations. Inherent challenges in such settings are (1) a trade-off between reward optimization and adhering to safety constraints, (2) partial observability, and (3) high-dimensional observations. We formalize the problem in a constrained, partially observable Markov decision process framework, where an agent obtains distinct reward and safety signals. To address the curse of dimensionality, we employ a novel safety critic using the stochastic latent actor-critic (SLAC) approach. The latent variable model predicts rewards and safety violations, and we use the safety critic to train safe policies. Using well-known benchmark environments, we demonstrate competitive performance over existing approaches with respects to computational requirements, final reward return, and satisfying the safety constraints.

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Yannick Hogewind, Thiago D. Simao, Tal Kachman, Nils Jansen. 2022-10-02. Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation. https://arxiv.org/abs/2210.01801

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