PTDRL: Parameter Tuning using Deep Reinforcement Learning

In their work, the context is a function Abstractly, a navigation system: C: X Θ A maps the of the lidar inputs. They use change-point-detection [22] state and parameter space to the action space. The state X to segment human-guided navigation trajectories into a prespecified is represented by the robot sensory inputs and information number of contexts. The robot recognizes its current about the world, such as the cost-map and next way-point. Figure 1: Original and reconstructed cost-maps of a physical experiment. The reconstruction captures the main details of the original cost-map, showing that the learnt latent space in the simulation can be used for the real world. The parameters space Θ is comprised of optimization parameters of the navigation system, robot constrains, etc. The action space A is a velocity vector (e.g., linear and angular Figure 1: A 3D representation of the value function at different velocity).