Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning.

We present a new approach for learning to solve SMT formulas. We phrase the challenge of solving SMT formulas as a tree search problem where at each step a transformation is applied to the input formula until the formula is solved.

Crucially, the neural model uses miniKanren's

The problem is undecidable and even practical instances are challenging.

While recent developments in autonomous vehicle (A V) technology highlight substantial progress, we lack tools for rigorous and scalable testing.

Neural Information Processing Systems http://nips.cc/

However, many real-world problems inherently have multiple goals, where multi-objective formulations are more natural.

We introduce two novel tree search algorithms that use a policy to guide search.

In this paper we build on the Optimal Rewards Framework of Singh et al. [2010] that defines the optimal intrinsic reward function as one that when used by an RL agent achieves behavior that optimizes the

Neural Information Processing Systems http://nips.cc/