Keywords: safety, reinforcement learning, safe reinforcement learning, constrained Markov decision process, partially observable Markov decision process, MDP, POMDP
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 regarding computational requirements, final reward return, and satisfying the safety constraints.
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TL;DR: This paper proposes Safe SLAC, a safety-constrained RL approach for partially observable settings, which uses a stochastic latent variable model combined with a safety critic.