Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation

• URL: http://arxiv.org/abs/2210.01801v1
• Date: Sun, 2 Oct 2022 19:55:42 GMT
• Title: Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
• Authors: Yannick Hogewind, Thiago D. Simao, Tal Kachman, Nils Jansen
• Abstract summary: We address the problem of safe reinforcement learning from pixel observations. We formalize the problem in a constrained, partially observable Markov decision process framework. We employ a novel safety critic using the latent actor-critic (SLAC) approach.
• Score: 3.5884936187733394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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