Collaborative Q(λ) Reinforcement Learning Algorithm - A Promising Robot Learning Framework

U. Kartoun, H. Stern, Y. Edan, C. Feied, J. Handler, M. Smith, and M. Gillam (USA)


Robot simulation, reinforcement learning, and navigation


This paper presents the design and implementation of a new reinforcement learning (RL) based algorithm. The proposed algorithm, )(λCQ (collaborative )(λQ ) allows several learning agents to acquire knowledge from each other. Acquiring knowledge learnt by an agent via collaboration with another agent enables acceleration of the entire learning system; therefore, learning can be utilized more efficiently. By developing collaborative learning algorithms, a learning task solution can be achieved significantly faster if performed by a single agent only, namely the number of learning episodes to solve a task is reduced. The proposed algorithm proved to accelerate learning in navigation robotic problem. The )(λCQ algorithm was applied to autonomous mobile robot navigation where several robot agents serve as learning processes. Robots learned to navigate an 11 x 11 world contains obstacles and boundaries choosing the optimum path to reach a target. Simulated experiments based on 50 learning episodes showed an average improvement of 17.02% while measuring the number of learning steps required reaching definite optimality and an average improvement of 32.98% for convergence to near optimality by using two robots compared with the )(λQ algorithm [1, 2].

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