In the case of VQAs, this procedure will introduce redundancy, but the variational properties of VQAs can naturally handle problems of over-rotation and under-rotation by updating the amplitude and frequency parameters.
We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (discontinuous) context changes occurring during an episode; and c) Markovian context evolution.
On the other hand, overfitting to an opponent (i. e., exploiting only one specific type of opponent) makes the learning player easily exploitable by others.
In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI).
Then, a set of mission performance evaluators is established to quantitatively assess the capability of the system in a comprehensive manner, including UAV navigation, passive SAR imaging and communication.
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world.
Automatic optimization of spoken dialog management policies that are robust to environmental noise has long been the goal for both academia and industry.