A Reinforcement Learning Approach for a Goal-Reaching Behavior
نویسندگان
چکیده
Developing elementary behavior is the starting point for the realization of complex systems. In this paper we win describe a learning algorithm that realizes a simple goal-reaching behavior for an autonomous vehicle when a-priori knowledge of the environment is not provided. The state of the system is based on information received by a visual sensor. A Q-learnlng algorithm associates the optimal action to each state, developing the optimal state-action rules (optimal policy). Afew training trials are sufficient, in simulation, to learn she optimal policy since during the test rials the set of actions is initially imited. The state and action sets are then enlarged, introducing fuzzy variables with their membership functions tothe extent of tackling errors in state stimation dueto the noise in the vision measurements. Experimental resuILs, both in simulated and real environment, areshown.
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