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SOAR: an architecture for general intelligence
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Planning under time constraints in stochastic domains
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Abstraction and approximate decision-theoretic planning
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Reinforcement learning with hierarchies of machines
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Multi-time models for temporally abstract planning
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
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Theoretical Results on Reinforcement Learning with Temporally Abstract Options
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Decomposition techniques for planning in stochastic domains
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Model minimization in Markov decision processes
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A new decomposition technique for solving Markov decision processes
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Structure in the Space of Value Functions
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Predictive state representations with options
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Causal Graph Based Decomposition of Factored MDPs
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Automatic shaping and decomposition of reward functions
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Hierarchical reinforcement learning with the MAXQ value function decomposition
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Robust combination of local controllers
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Flexible decomposition algorithms for weakly coupled Markov decision problems
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We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and macro-actions and leave the state space unchanged, we propose a hierarchical model (using an abstract MDP) that works with macro-actions only, and that significantly reduces the size of the state space. This is achieved by treating macroactions as local policies that act in certain regions of state space, and by restricting states in the abstract MDP to those at the boundaries of regions. The abstract MDP approximates the original and can be solved more efficiently. We discuss several ways in which macro-actions can be generated to ensure good solution quality. Finally, we consider ways in which macro-actions can be reused to solve multiple, related MDPs; and we show that this can justify the computational overhead of macro-action generation.