Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling

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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 benefits of the hybrid approach are evaluated using a repeatable job shop scheduling task.