Monte Carlo Tree Search (MCTS) methods have proven powerful in planning for sequential
decision-making problems such as Go and video games, but their performance can be poor …

Bandit algorithms, named after casino slot machines sometimes known as “one-armed
bandits”, fall into a broad category of stochastic scheduling problems. In the setting with …

This paper proposes and evaluates Memory-Augmented Monte Carlo Tree Search (M-
MCTS), which provides a new approach to exploit generalization in online real-time search …

Existing retrieval systems rely on a single active query to pull documents from the index.
Relevance feedback may be used to iteratively refine the query, but only one query is active …

Recent work has begun exploring the value of domain abstractions in Monte-Carlo Tree
Search (MCTS) algorithms for probabilistic planning. These algorithms automatically …

Training a classifier with high mean accuracy from a manifold-distributed dataset can be
challenging. This problem is compounded further when there are only few labels available …

One of the fundamental problems in Artificial Intelligence is sequential decision making in a
flexible environment. Reinforcement Learning (RL) gives a set of tools for solving sequential …

Reinforcement learning (RL) defines a general computational problem where the learner
must learn to make good decisions through interactive experience. To be effective in solving …

Recently, a new language is proposed to model probabilistic planning. It is the Relational
Dynamic Influence Diagram Language (RDDL). There are a few domains, which are …

The problem of sequential decision making under uncertainty, often modeled as an MDP is
an important problem in planning and reinforcement learning communities. Traditional MDP …