Learning based Model Predictive Control for Safe Reinforcement Learning
user-5f03edee4c775ed682ef5237(2019)
摘要
Reinforcement learning has been successfully used to solve difficult tasks in complex unknown environments. However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications. In this paper, we attempt to bridge the gap between learning-based techniques that are scalable and highly autonomous but often unsafe and robust control techniques, which have a solid theoretical foundation that guarantees safety but often require extensive expert knowledge to identify the system and estimate disturbance sets. We combine a provably safe learning-based MPC scheme that allows for input-dependent uncertainties with techniques from model-based RL to solve tasks with only limited prior knowledge. We evaluate the resulting algorithm to solve a reinforcement learning task in a simulated cart-pole dynamical system with safety constraints.
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