Agent's actions as a classification criteria for the state space in a learning from rewards system
We focus in this paper oil the problem of learning an autonomous agent's policy when the state space is very large and the set of actions available is comparatively short. To this end, we use it non-parametric decision rule (concretely, a nearest-neighbour Strategy) in order to Cluster the state space by means of the action that leads to a Successful Situation. Using an exploration strategy to avoid greedy behaviour, the agent builds clusters of positively-classified States through trial and error learning. In this paper, we implement a 3D synthetic agent which plays all,avoid the asteroid' game that suits our assumptions. Using as the state space 11 feature vector space extracted from a Visual navigation system, we test two exploration strategies using the trial and error learning method. This experiment shows that the agent is a good classifier over the state space, and will therefore show good behaviour in its synthetic world.
classification by actions intelligent agents state space's clustering