Safer reinforcement learning for robotics

Reinforcement learning is an active research area in the fields of artificial intelligence and machine learning, with applications in control. The most important feature of reinforcement learning is its ability to learn without prior knowledge about the system. However, in the real world, reinforcement learning actions may lead to serious damage...

Benchmarking model-free and model-based optimal control

Model-free reinforcement learning and nonlinear model predictive control are two different approaches for controlling a dynamic system in an optimal way according to a prescribed cost function. Reinforcement learning acquires a control policy through exploratory interaction with the system, while nonlinear model predictive control exploits an...

Evaluation of physical damage associated with action selection strategies in reinforcement learning

Reinforcement learning techniques enable robots to deal with their own dynamics and with unknown environments without using explicit models or preprogrammed behaviors. However, reinforcement learning relies on intrinsically risky exploration, which is often damaging for physical systems. In the case of the bipedal walking robot Leo, which is...