49 citations · 127 across the 28 of their papers we have counts for
7 papers · 1 filter
Decision Making Agent Searching for Markov Models in Near-Deterministic World
Gabor Matuz, Andras Lorincz
Reinforcement learning has solid foundations, but becomes inefficient in partially observed (non-Markovian) environments. Thus, a learning agent -born with a representation and a p…
The many faces of optimism - Extended version
István Szita, András Lőrincz
The exploration-exploitation dilemma has been an intriguing and unsolved problem within the framework of reinforcement learning. "Optimism in the face of uncertainty" and model bui…
Factored Value Iteration Converges
Istvan Szita, Andras Lorincz
In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solution of factored Markov decision processes (fMDPs). The traditional approximate…
Applying Policy Iteration for Training Recurrent Neural Networks
I. Szita, A. Lorincz
Recurrent neural networks are often used for learning time-series data. Based on a few assumptions we model this learning task as a minimization problem of a nonlinear least-square…
Kalman-filtering using local interactions
Barnabas Poczos, Andras Lorincz
There is a growing interest in using Kalman-filter models for brain modelling. In turn, it is of considerable importance to represent Kalman-filter in connectionist forms with loca…
Temporal plannability by variance of the episode length
Balint Takacs, Istvan Szita, Andras Lorincz
Optimization of decision problems in stochastic environments is usually concerned with maximizing the probability of achieving the goal and minimizing the expected episode length.…
Searching for Plannable Domains can Speed up Reinforcement Learning
Istvan Szita, Balint Takacs, Andras Lorincz
Reinforcement learning (RL) involves sequential decision making in uncertain environments. The aim of the decision-making agent is to maximize the benefit of acting in its environm…