Before revision:  Making decisions for maximal reward in a stochastic setting requires learning values and constructing policies over a belief space, i.e., probability distribution of the robot-world state. Value learning over belief spaces suffer from computational challenges in high-dimensional spaces, such as large spatial environments and long temporal horizons for exploration.
After revision:  Making decisions for maximal reward in a stochastic setting requires learning values and constructing policies over a belief space, i.e., probability distribution of the robot-world states. However, belief space planning in a large spatial environment over long temporal horizons for exploration.