Tamás Bécsi

associate professor

Budapest University of Technology and Economics

Hungary

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About

Tamás Bécsi is an associate professor at the Budapest University of Technology and Economics in Hungary. His research interests include reinforcement learning, mobile robot localization, and cooperative systems for automotive perception. Recent publications have focused on policy improvement in reinforcement learning, advanced motion control techniques, and optimization methods for lane keeping and highway control.

Articles (20)

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2025

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2022

Collaborators (3)

Szilárd Aradi

Budapest University of Technology and Economics

HUNGARY

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Domokos Esztergár-Kiss

Budapest University of Technology and Economics

HUNGARY

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Peter Gaspar

Institute for Computer Science and Control

HUNGARY

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Tamás Bécsi

Research Interests

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About

Articles (20)

MCTS-Based Policy Improvement for Reinforcement Learning

Exact particle flow Daum-Huang filters for mobile robot localization in occupancy grid maps

Path planning via reinforcement learning with closed-loop motion control and field tests

Rapidly Exploring Random Trees Reinforcement Learning (RRT-RL): A New Era in Training Sample Diversity

A Robust and Runtime-Efficient Track-to-Track Fusion for Automotive Perception Systems

Learn to Adapt: A Policy for History-Based Online Adaptation

Distributed Highway Control: A Cooperative Reinforcement Learning-Based Approach

Semi-Automatic BEV Ground Truth Generation for Automotive Perception Systems

Cooperative MARL-PPO Approach for Automated Highway Platoon Merging

Beyond Trial and Error: Lane Keeping with Monte Carlo Tree Search-Driven Optimization of Reinforcement Learning

Analytic solution of the exact Daum–Huang flow equation for particle filters

Traffic Signal Control with Successor Feature-Based Deep Reinforcement Learning Agent

Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach

Object-Level Data-Driven Sensor Simulation for Automotive Environment Perception

Skills to Drive: Successor Features for Autonomous Highway Pilot

Online Trajectory Planning with Reinforcement Learning for Pedestrian Avoidance

GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System

Environment Representations of Railway Infrastructure for Reinforcement Learning-Based Traffic Control

Quasi-Linear Parameter Varying Modeling and Control of an Electromechanical Clutch Actuator

Performance Evaluation of a Maneuver Classification Algorithm Using Different Motion Models in a Multi-Model Framework

Collaborators (3)

Szilárd Aradi

Domokos Esztergár-Kiss

Peter Gaspar

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