Integrating Sample-Based Planning and Model-Based Reinforcement Learning

Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, despite their exponentially large state spaces. Unfortunately, these algorithms all require access to a planner that computes a near optimal policy, and while many traditional MDP algorithms make this guarantee, their computation time grows with the number of states. We show how to replace these over-matched planners with a class of sample-based planners--whose computation time is independent of the number of states--without sacrificing the sampleefficiency guarantees of the overall learning algorithms. To do so, we define sufficient criteria for a sample-based planner to be used in such a learning system and analyze two popular sample-based approaches from the literature. We also introduce our own sample-based planner, which combines the strategies from these algorithms and still meets the criteria for integrati...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Thomas J. Walsh, Sergiu Goschin, Michael L. Littman
• Keywords: AAAI 2010, Computation Time, Intelligent Agents, Sample-based, Sample-based Planner