Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation

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 regarding computational requirements, final reward return, and satisfying the safety constraints.

Authors

• Yannick Hogewind
• Thiago D. Simão
• Tal Kachman
• Nils Jansen 0001

Keywords

• safety
• reinforcement learning
• safe reinforcement learning
• constrained Markov decision process
• partially observable Markov decision process
• MDP
• POMDP