  Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods,suchas
recurrent neural nets or history-based state abstraction, perform better than
their memory-less counterparts, due to the fact that function approximation in
Markov decision processes (MDP) can be viewed as inducing a Partially
observable MDP. However, there has been little formal analysis of such
history-based algorithms, as most existing frameworks focus exclusively on
memory-less features. In this paper, we introduce a theoretical framework for
studying the behaviour of RL algorithms that learn to control an MDP using
history-based feature abstraction mappings. Furthermore, we use this framework
to design a practical RL algorithm and we numerically evaluate its
effectiveness on a set of continuous control tasks.

Machine Learning;Systems and Control;Systems and Control;Machine Learning