Incremental topological reinforcement learning agent in non-structured environments

This paper describes a new reinforcement learning (RL) model, the incremental topological reinforcement learning agent (ITRLA), designed to guide agent navigation in non-structured environments, considering two common situations: (i) insertion of noise during state estimation and (ii) changes in environment structure. Tasks in non-structured environments are hard to be learned by traditional RL algorithms due to the stochastic state transitions. Such tasks are often modeled as partially observable Markov decision processes (POMDP), an expensive computational process. The main contribution of the ITRLA is to handle the two mentioned situations in non-structured environments with a reduced number of trials, and avoiding POMDP modeling.