  This paper proposes a detailed and extensive comparison of the Trust Region
Policy Optimization and DeepQ-Network with Normalized Advantage Functions with
respect to other state of the art algorithms, namely Deep Deterministic Policy
Gradient and Vanilla Policy Gradient. Comparisons demonstrate that the former
have better performances then the latter when asking robotic arms to accomplish
manipulation tasks such as reaching a random target pose and pick &placing an
object. Both simulated and real-world experiments are provided. Simulation lets
us show the procedures that we adopted to precisely estimate the algorithms
hyper-parameters and to correctly design good policies. Real-world experiments
let show that our polices, if correctly trained on simulation, can be
transferred and executed in a real environment with almost no changes.
