Learning State And Action Space Hierarchies For Reinforcement Learning Using
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Autonomous systems are often dicult to program. Reinforcement learning (RL) is an attractive alternative, as it allows the agent to learn behavior on the basis of sparse, delayed reward signals provided only when the agent reaches desired goals. Recent attempts to address the dimensionality of RL have turned to principled ways of exploiting temporal abstraction, where decisions are not required at each step, but rather invoke the execution of temporally-extended activities which follow their own policies un- til termination. This leads naturally to hierarchical control architectures and associated learning algorithms. This dissertation reviews several approaches to temporal abstraction and hierarchical or- ganization that machine learning researchers have recently developed and presents a new method for the autonomous construction of hierarchical action and state representations in reinforcement learning, aimed at accelerating learning and extending the scope of such systems. In this approach, the agent uses information acquired while learning one task to discover subgoals for similar tasks. The agent is able to transfer knowledge to subsequent tasks and to accelerate learning by creating useful new subgoals and by o-line learning of corresponding subtask policies as abstract actions (options). At the same time, the subgoal actions are used to construct a more abstract state repre- sentation using action-dependent state space partitioning. This representation forms a new level in the state space hierarchy and serves as the initial representation for new learning tasks. In order to ensure that tasks are learnable, value functions are built simultaneously at dierent levels of the hierarchy and inconsistencies are used to identify actions to be used to refine relevant portions of the abstract state space. Together, these techniques permit the agent to form more abstract action and state representations over time. Experiments in deterministic and stochastic domains show that the presented method can significantly outperform learning on a flat state space representation.