Iteratively extending time horizon reinforcement learning

Iteratively extending time horizon reinforcement learning
Proceedings of ECML 2003 (Postscript - 6 KB )

Abstract: Reinforcement learning aims to determine an (infinite time horizon) optimal control policy from interaction with a system. It can be solved by approximating the so-called $Q$-function from a sample of four-tuples $(x_t, u_t , r_t, x_{t+1})$ where $x_t$ denotes the system state at time $t$, $u_t$ the control action taken, $r_t$ the instantaneous reward obtained and $x_{t+1}$ the successor state of the system, and by determining the optimal control from the $Q$-function. Classical reinforcement learning algorithms use an ad hoc version of stochastic approximation which iterates over the $Q$-function approximations on a four-tuple by four-tuple basis. In this paper, we reformulate this problem as a sequence of batch mode supervised learning problems which in the limit converges to (an approximation of) the $Q$-function. Each step of this algorithm uses the full sample of four-tuples gathered from interaction with the system and extends by one step the horizon of the optimality criterion. An advantage of this approach is to allow the use of standard batch mode supervised learning algorithms, instead of the incremental versions used up to now. In addition to a theoretical justification the paper provides empirical tests in the context of the ``Car on the Hill'' control problem based on the use of ensembles of regression trees. The resulting algorithm is in principle able to handle efficiently large scale reinforcement learning problems.