Papers › Constrained Policy Optimization via Bayesian World Models

Constrained Policy Optimization via Bayesian World Models

24 Jan 2022ICLR 2022 4arXiv:2201.09802archive 2025-07-28

Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas Krause

Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications. We propose LAMBDA, a novel model-based approach for policy optimization in safety critical tasks modeled via constrained Markov decision processes. Our approach utilizes Bayesian world models, and harnesses the resulting uncertainty to maximize optimistic upper bounds on the task objective, as well as pessimistic upper bounds on the safety constraints. We demonstrate LAMBDA's state of the art performance on the Safety-Gym benchmark suite in terms of sample efficiency and constraint violation.

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