Reinforcement Learning for Agents with Many Sensors and Actuators Acting in Categorizable Environments

In this paper, we confront the problem of applying reinforcement learning to agents that perceive the environment through many sensors and that can perform parallel actions using many actuators as is the case in complex autonomous robots. We argue that reinforcement learning can only be successfully applied to this case if strong assumptions are made on the characteristics of the environment in which the learning is performed, so that the

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Enric Celaya, Josep M. Porta
• Keywords: CORR 2011, Education, Parallel Actions, Reinforcement, Strong Assumptions