create a space of molecules of its ocnstructions, and use active learning to search through it.

Create an approximation of the value function \(U_{\phi}\) using Approximate Value Function, and use Policy Gradient to optimize an monte-carlo tree search policy

How do you sample particle filters? This doesn’t work for a continuous action space.

Contributions

• Uses KLD sampling—adaptive sampling of particple filters
• “belief packing”—pack similar beliefs together, making observation tree smaller

KLD Sampling

KLD Sampling uses KL Divergence to approximate difference between two probability distributions:

\begin{equation} N \approx \frac{k-1}{2\xi} \qty(1- \frac{2}{9(k-1)} + \sqrt{\frac{2}{9(k-1)}} z_{1-\eta})^{3} \end{equation}

“Propagation”

We want to get a set of sampled observations from belief + action.

Belief Packing

L1 norm between beliefs. If its too small consider them the same beliefs.