This paper studies the deception applied on agent in a partially observable Markov decision
process. We introduce deceptive kernel function (the kernel) applied to agent's observations …

A common approach to solve partially observable Markov decision processes (POMDPs) is
transforming them into Makov decision processes (MDPs) defined on information states …

Partially Observable Markov Decision Processes (POMDPs) are rich environments often
used in machine learning. But the issue of information and causal structures in POMDPs has …

Systems modeled as partially observable Markov decision processes (POMDPs) can be
tracked quickly with three restrictions: all actions are grouped together, the out-degree of …

This study extends the framework of partially observable Markov decision processes
(POMDPs) to allow their parameters, ie, the probability values in the state transition functions …

Partially Observable Markov Decision Process (POMDP) is a framework applicable to many
real world problems. In this work, we propose an approach to solve POMDPs with …

We present a method for identifying actions that lead to observations which are only weakly
informative in the context of partially observable Markov decision processes (POMDP). We …

This paper introduces novel concepts for accelerating learning in an off-policy reinforcement
learning algorithm for Partially Observable Markov Decision Processes (POMDP) by …

Markov decision processes (MDP's) Bellman, 1957] are a mathematical formalization of
problems in which a decision-maker, or agent, must choose how to act to maximize its …

Complex behaviors are often driven by an internal model, which integrates sensory
information over time and facilitates long-term planning. Inferring the internal model is a …