Strategies for simulating pedestrian navigation with multiple reinforcement learning agents

Abstract In this paper, a new Multi-agent Reinforcement Learning (MARL) approach is introduced for the simulation of pedestrian groups. Unlike other solutions, where the behaviors of the pedestrians are coded in the system, in our approach the agents learn by interacting with the environment. The embodied agents must learn to control their velocity, avoiding obstacles and the other pedestrians, to reach a goal inside the scenario. The main contribution of this paper is to propose this new methodology that uses different iterative learning strategies, combining a Vector Quantization (state space generalization) with the Q-learning algorithm (VQQL). Two algorithmic schemas, Iterative VQQL (ITVQQL) and Incremental (INVQQL), which differ in the way of addressing the problems, have been designed and used with and without transfer of knowledge. These algorithms are tested and compared with the VQQL algorithm as a baseline in two scenarios where agents need to solve well-known problems in p...

• Type: Journal
• Year: 2015
• Where: AAMAS
• Authors: Francisco Martinez-Gil, Miguel Lozano, Fernando Fernández
• Keywords: AAMAS 2015, Intelligent Agents