Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling

We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of executing a particular task on state, time and reward using a general procedural format (pure stochastic function). Secondly, we show that effective planning under uncertainty can be obtained by combining adaptive horizon stochastic planning with reinforcement learning (RL) in a hybrid system. The bene

• Type: Conference
• Year: 2006
• Where: AIPS
• Authors: Malcolm J. A. Strens
• Keywords: AIPS 2006, Artificial Intelligence, Horizon Stochastic Planning, Pure Stochastic Function, Stochastic Task Model