Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robotDownload PDF

Jun 08, 2020L4DC 2020Readers: Everyone
  • Abstract: Model Predictive Control (MPC) is a powerful control technique that handles constraints, takes the system’s dynamics into account, and is optimal with respect to a given cost function. In practice, however, it often requires an expert to craft and tune this cost function and find trade-offs between different state penalties to satisfy simple high level objectives. In this paper, we use Reinforcement Learning and in particular value learning to approximate the value function given only high level objectives, which can be sparse and binary. Building upon previous works, we present improvements that allowed us to successfully deploy the method on a real world unmanned ground vehicle. Our experiments show that our method can learn the cost function from scratch and without human intervention, while reaching a performance level similar to that of an expert-tuned MPC. We perform a quantitative comparison of these methods with standard MPC approaches both in simulation and on the real robot.
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