Papers › Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation

Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation

2 Oct 2022arXiv:2210.01801archive 2025-07-28

Yannick Hogewind, Thiago D. Simao, Tal Kachman, Nils Jansen

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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build_mlp safe-slac/safe-slac/slac/utils.py official repository unverified MIT (permissive) · bcd2f30df5201848 · report
create_feature_actions safe-slac/safe-slac/slac/utils.py official repository unverified MIT (permissive) · c17d4065283d4027 · report
make_safety safe-slac/safe-slac/slac/env.py official repository unverified MIT (permissive) · c63d0b541a34fef9 · report
sample_reproduction safe-slac/safe-slac/slac/utils.py official repository unverified MIT (permissive) · f958d15bc44a28f7 · report

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Reinforcement LearningReinforcement Learning (RL)Safe Reinforcement Learningreinforcement-learning

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